Index

A

• abstraction, Scala
  • about / Abstraction
  • higher-kind projection / Higher-kind projection
  • covariant functors for vectors / Covariant functors for vectors
  • contravariant functors for co-vectors / Contravariant functors for co-vectors
  • monads / Monads
• Actor model
  • about / The Actor model
  • components / The Actor model
• actors
  • about / Scalability
• adaptive modeling / Model categorization
• Akka.io
  • about / An overview
• Akka framework
  • about / An overview, Akka
  • URL / Akka
  • master-workers / Master-workers
  • futures / Futures
• Algebird
  • about / Abstraction
• algebraic libraries
  • about / Algebraic and numerical libraries
  • jBlas 1.2.3 / Algebraic and numerical libraries
  • Colt 1.2.0 / Algebraic and numerical libraries
  • AlgeBird 2.10 / Algebraic and numerical libraries
  • Breeze 0.8 / Algebraic and numerical libraries
• alternative preprocessing techniques
  • autoregressive models / Alternative preprocessing techniques
  • curve-fitting algorithms / Alternative preprocessing techniques
  • nonlinear dynamic systems / Alternative preprocessing techniques
  • Hidden Markov models / Alternative preprocessing techniques
• annual dividend yield
  • about / Fundamental analysis
• Apache Commons Math
  • URL / Don't reinvent the wheel!
  • about / Apache Commons Math
  • description / Description
  • licensing / Licensing
  • installation / Installation
  • installation, for Mac OS X / Installation
  • installation, for Windows / Installation
• Apache Spark
  • about / Apache Spark
  • features / Why Spark?
  • deign principles / Design principles
  • deployment modes / Deploying Spark
  • performance evaluation / Performance evaluation
  • pros / Pros and cons
  • cons / Pros and cons
• Apache Spark (Akka)
  • about / Scalability
• artificial neural networks
  • feed-forward neural networks / Feed-forward neural networks
  • advantages / Benefits and limitations
  • disadvantages / Benefits and limitations
• autonomous systems / The problem
• Autoregressive Integrated Moving Average (ARIMA) / Alternative preprocessing techniques
• Autoregressive Moving Average (ARMA) / Alternative preprocessing techniques

B

• batch gradient descent algorithm / Selecting an optimizer
• batch training / Online training versus batch training
• Baum-Welch estimator
  • about / The Baum-Welch estimator (EM)
• Bayesian network
  • about / Probabilistic graphical models
• Berkeley Data Analytics Stack (BDAS)
  • reference / Apache Spark
• Bernoulli mixture model
  • about / Model
• Bernoulli model
  • about / The Multivariate Bernoulli classification
• bias-variance decomposition
  • about / Bias-variance decomposition
• bias input / Mathematical background
• binary SVC
  • about / The binary SVC
  • LIBSVM / LIBSVM
  • design / Design
  • configuration parameters / Configuration parameters
  • interface to LIBSVM / Interface to LIBSVM
  • training / Training
  • classification / Classification
  • c-penalty and margin / C-penalty and margin
  • kernel evaluation / Kernel evaluation
  • applications in risk analysis / Applications in risk analysis
• Breeze Scala libraries / Abstraction
• Broyden-Fletcher-Goldfarb-Shanno (BGFS) / BFGS

C

• C-Epsilon SVM formulation / The nonseparable case – the soft margin
• cake pattern
  • about / Configurability
  / Step 3 – instantiation
• case classes
  • versus companion objects / Companion objects versus case classes
  • versus enumerations / Enumerations versus case classes
  • advantages / Enumerations versus case classes
• cash per share
  • about / Fundamental analysis
• categories, NP problems
  • about / NP problems
  • P-problems / NP problems
  • NP problems / NP problems
  • NP-complete problems / NP problems
  • NP-hard problems / NP problems
• centroid / K-means clustering
• Cholesky decomposition
  • about / Cholesky factorization
• Cholesky factorization
  • about / Cholesky factorization
• chromosomes / Evolutionary computing
• class constructor template
  • about / Class constructor template
• classification model, evaluation factors
  • accuracy / Key quality metrics
  • precision / Key quality metrics
  • recall / Key quality metrics
  • F-measure or F-score F / Key quality metrics
  • G-measure / Key quality metrics
• classification model, terminology
  • true positives (TP) / Key quality metrics
  • true negatives (TN) / Key quality metrics
  • false positives (FP) / Key quality metrics
  • false negatives (FN) / Key quality metrics
• class prior
  • about / Formalism
• class prior probability
  • about / Formalism
• cluster assignment, K-means clustering
  • about / Step 2 – cluster assignment
• cluster configuration, K-means clustering
  • about / Step 1 – cluster configuration
  • clusters, defining / Defining clusters
  • clusters, initializing / Initializing clusters
• clustering
  • about / Clustering
  • expectation-maximization algorithm / The expectation-maximization algorithm
• clustering algorithms
  • K-means clustering / Clustering, K-means clustering
  • EM / Clustering
• co-vector
  • about / Higher-kind projection
• code snippets
  • format / Code snippets format
• common discriminative kernels
  • about / Common discriminative kernels
• companion objects
  • versus case classes / Companion objects versus case classes
• complex adaptive systems / Introduction to LCS
• components, XCS
  • about / XCS components
  • application to portfolio management / Application to portfolio management, The XCS core data
  • XCS rules / XCS rules
  • covering / Covering
  • implementation example / An implementation example
• computational workflow
  • overview / An overview of computational workflows
• conditional dependency / Training
• conditional independence / A model by any other name
  • about / Probabilistic graphical models
• conditional random field (CRF)
  • about / Conditional random fields, Introduction to CRF
  • linear chain CRF / Linear chain CRF
  • potential functions / Linear chain CRF
  • identity potential functions / Linear chain CRF
  • transition feature functions / Linear chain CRF
  • state feature functions / Linear chain CRF
  • text analytics / Regularized CRFs and text analytics
  • versus HMM / Comparing CRF and HMM
• configurability
  • about / Configurability
• configuration parameters, SVM
  • SVM formulation / The SVM formulation
  • SVM kernel function / The SVM kernel function
  • SVM execution / The SVM execution
• confusion matrix / F-score for multinomial classification
• conjugate directions
  • about / Conjugate gradient
• conjugate gradient
  • about / Conjugate gradient
• connectionism
  • about / The biological background
• constructive tuning strategy / Regularization
• Consumer Price Index (CPI)
  • about / Fundamental analysis
• consumer price index (CPI)
  • about / Introducing the multinomial Naïve Bayes
• continuation-passing style (CPS) / Beyond actors – reactive programming
• control learning / A solution – Q-learning
• convolution neural networks
  • about / Convolution neural networks
  • local receptive fields / Local receptive fields
  • weights, sharing / Sharing of weights
  • convolution layers / Convolution layers
  • subsampling layers / Subsampling layers
  • fully connected hidden layer and output layer / Putting it all together
• core parking
  • about / Performance evaluation
• Counter class
  • about / Counter
• covariant functor
  • about / Covariant functors for vectors
• cross-validation, model
  • about / Cross-validation
  • one-fold cross validation / One-fold cross validation
  • K-fold cross validation / K-fold cross validation
• crossover operator, genetic algorithm implementation
  • about / Crossover
  • population / Population
  • chromosomes / Chromosomes
  • genes / Genes
• curve fitting
  • about / Supervised learning

D

• Darwinian process / The origin
• data, profiling
  • about / Profiling data
  • immutable statistics / Immutable statistics
  • Z-score / Z-Score and Gauss
• data chunks / 0xdata Sparkling Water
• data clustering
  • about / Clustering
• data elements / 0xdata Sparkling Water
• data extraction
  • about / Data extraction
• data frames / 0xdata Sparkling Water
• data partitioning
  • about / Clustering
• data segmentation
  • about / Clustering
• DataSourceConfig class
  • pathName parameter / Data extraction
  • normalize parameter / Data extraction
  • reverseOrder parameter / Data extraction
  • headerLines parameter / Data extraction
• DBpedia / Basics of information retrieval
• decision-making agent / Concepts
• decision boundary / Plotting data
• decoding, hidden Markov model (HMM)
  • about / Decoding – CF-3
  • Viterbi algorithm / The Viterbi algorithm
• def
  • about / Understanding the problem
• dependency injection
  • about / Configurability
• deployment modes, Spark
  • standalone / Deploying Spark
  • local / Deploying Spark
  • Yarn clusters manager / Deploying Spark
  • Apache Mesos resource manager / Deploying Spark
• descriptive models / Model categorization
• designing
  • about / Model versus design
• design principles, Spark
  • about / Design principles
  • in-memory persistency / In-memory persistency
  • laziness / Laziness
  • transforms / Transforms and actions
  • actions / Transforms and actions
  • shared variables / Shared variables
• design template, for classifiers
  • about / Design template for immutable classifiers
• destructive tuning strategy / Regularization
• DFT-based filtering
  • about / DFT-based filtering
• dimension reduction
  • about / Dimension reduction, Dimension reduction
  • principal components analysis / Principal components analysis
  • non-linear models / Non-linear models
• directed graphical models
  • about / Probabilistic graphical models
• discrete Fourier transform (DFT)
  • about / Discrete Fourier transform
  / PCA
• discrete Kalman filter
  • about / The discrete Kalman filter
  • recursive algorithm / The discrete Kalman filter, The recursive algorithm
  • optimal estimator / The discrete Kalman filter
  • state space estimation / The state space estimation
  • benefits / Benefits and drawbacks
  • drawbacks / Benefits and drawbacks
  • alternative preprocessing techniques / Alternative preprocessing techniques
• discretization / Value encoding
• dividend coverage ratio
  • about / Fundamental analysis
• DMatrix class
  • about / DMatrix class
• DNA / Evolutionary computing
• Domain Specific Languages (DSL)
  • about / Maintainability
• dynamic programming
  • about / Overview of dynamic programming

E

• earnings per share (EPS)
  • about / Fundamental analysis
• Eigenvalue decomposition
  • about / Eigenvalue decomposition
• encapsulation
  • about / Encapsulation
  • package scope / Encapsulation
  • class or object scope / Encapsulation
• encoding scheme, genetic encoding
  • about / The encoding scheme
  • flat encoding / Flat encoding
  • hierarchical encoding / Hierarchical encoding
• enumerations
  • versus case classes / Enumerations versus case classes
  • advantages / Enumerations versus case classes
• epoch / The training epoch
• Erlang programming language / The Actor model
• error backpropagation, training epoch
  • about / Step 2 – error backpropagation
  • weights' adjustment / Weights' adjustment
  • error propagation / The error propagation
  • computational model / The computational model
• error handling, monadic data transformation
  • about / Error handling
  • input value / Error handling
  • output value / Error handling
• error insensitive zone
  • about / An overview
• evaluation
  • about / Evaluation
  • execution profile / The execution profile
  • impact of learning rate / Impact of the learning rate
  • impact of momentum factor / The impact of the momentum factor
  • impact of number of hidden layers / The impact of the number of hidden layers
  • test case / Test case
• evaluation, hidden Markov model (HMM)
  • about / Evaluation – CF-1
  • alpha algorithm / Alpha – the forward pass
  • beta algorithm / Beta – the backward pass
• evidence
  • about / Formalism
• evolution
  • about / Evolution
  • origin / The origin
  • NP problems / NP problems
  • ary computing / Evolutionary computing
• exchange-traded funds (ETFs) / Test case
• ExecutionContextTaskSupport
  • about / Processing a parallel collection
• expectation-maximization (EM)
  • about / Training – CF-2
• expectation-maximization algorithm
  • about / The expectation-maximization algorithm
  • Gaussian mixture models / Gaussian mixture models
  • overview / Overview of EM
  • implementation / Implementation
  • classification / Classification
  • testing / Testing
  • online EM algorithm / The online EM algorithm
• experimenting, with Spark
  • about / Experimenting with Spark
  • Spark, deploying / Deploying Spark
  • Spark shell, using / Using Spark shell
  • MLlib / MLlib
  • RDD generation / RDD generation
  • K-means, using Spark / K-means using Spark
• exponential moving average
  • about / The exponential moving average
• exponential normalization / Softmax
• extended Kalman filter (EKF) / Benefits and drawbacks
• Extended Kalman Filters (EKF) / The discrete Kalman filter
• extended learning classifier systems
  • about / Extended learning classifier systems
  • exploration phase / Extended learning classifier systems
  • exploitation phase / Extended learning classifier systems
  • components / XCS components

F

• -fold cross validation / K-fold cross validation
• F-score for binomial classification
  • about / F-score for binomial classification
• F-score for multinomial classification
  • about / F-score for multinomial classification
  • macro method / F-score for multinomial classification
  • micro method / F-score for multinomial classification
• Fast Fourier Transform (FFT)
  • about / Discrete Fourier transform
• features extraction
  • about / Extracting features
• features maps / Sharing of weights
• features selection
  • about / Selecting features
• Federal Fund rate
  • about / Fundamental analysis
• Federal fund rate (FDF)
  • about / Introducing the multinomial Naïve Bayes
• feed-forward neural network (FFNN) / The biological background
• feed-forward neural networks
  • about / Feed-forward neural networks
  • biological background / The biological background
  • mathematical background / Mathematical background
• FFNN without a hidden layer / The multilayer perceptron
• finances 101
  • about / Finances 101
  • fundamental analysis / Fundamental analysis
  • technical analysis / Technical analysis
  • options trading / Options trading
  • financial data sources / Financial data sources
• first order predicate logic
  • about / First order predicate logic
• fitness functions, genetic algorithms
  • about / The fitness score
  • fixed fitness function / The fitness score
  • evolutionary fitness function / The fitness score
  • approximate fitness function / The fitness score
• fixed lag smoothing / Fixed lag smoothing
• fork-join pool
  • about / Processing a parallel collection
• ForkJoinTaskSupport
  • about / Processing a parallel collection
• Fourier analysis
  • about / Fourier analysis
  • discrete Fourier transform (DFT) / Discrete Fourier transform
  • DFT-based filtering / DFT-based filtering
  • market cycles, detecting / Detection of market cycles
• Fourier transform
  • about / Fourier analysis
• frameworks
  • about / Tools and frameworks
• frequency domain
  • about / Discrete Fourier transform
• fully connected neural network / The network topology
• function approximation
  • about / Supervised learning
  / Quantization
• functors
  • about / Abstraction
• fundamental analysis
  • about / Fundamental analysis
• futures, Akka framework
  • about / Futures
  • Actor life cycle / The Actor life cycle
  • blocking on / Blocking on futures
  • future callbacks, handling / Handling future callbacks

G

• Gauss-Newton technique
  • about / Gauss-Newton
• generalized autoregressive conditional heteroscedasticity (GARCH) / Alternative preprocessing techniques
• generic Lp -norm
  • about / Ln roughness penalty
• genes / Evolutionary computing
• genetic algorithms
  • about / Genetic algorithms and machine learning
  • discrete model parameters / Genetic algorithms and machine learning
  • reinforcement learning / Genetic algorithms and machine learning
  • neural network architecture / Genetic algorithms and machine learning
  • ensemble learning / Genetic algorithms and machine learning
  • components / Genetic algorithm components
  • fitness score / The fitness score
  • implementation / Implementation
  • tests / Tests
  • advantages / Advantages and risks of genetic algorithms
  • disadvantages / Advantages and risks of genetic algorithms
• genetic algorithms, for trading strategies
  • about / GA for trading strategies
  • trading strategies, defining / Definition of trading strategies
  • test case / A test case
• genetic encoding
  • about / Genetic algorithm components, Encoding
  • value encoding / Value encoding
  • predicate encoding / Predicate encoding
  • solution encoding / Solution encoding
  • encoding scheme / The encoding scheme
• genetic fitness functions
  • about / Genetic algorithm components
• genetic operators
  • about / Genetic algorithm components, Genetic operators
  • selection / Genetic operators, Selection
  • crossover / Genetic operators, Crossover
  • mutation / Genetic operators, Mutation
  • transposition operator / Genetic operators
• GNU Lesser General Public License (LGPL) / Licensing
• GoogleFinancials / Data sources
• gradient descent / Ordinary least squares regression
• gradient descent methods
  • about / Steepest descent
  • steepest descent / Steepest descent
  • conjugate gradient / Conjugate gradient
  • stochastic gradient descent / Stochastic gradient descent
• graph-structured CRF / Introduction to CRF
• graphical models / Probabilistic graphical models
• gross domestic product (GDP)
  • about / Introducing the multinomial Naïve Bayes
• Growth Domestic Product (GDP)
  • about / Fundamental analysis

H

• Hadoop Distributed File System (HDFS) / Step 2 – loading data
• Hadoop distributed file system (HDFS) / Apache Spark
• hard margin / The separable case – the hard margin
• Hessian matrix
  • about / Jacobian and Hessian matrices
• hidden layers / The multilayer perceptron
• hidden Markov model (HMM)
  • about / The hidden Markov model
  • components / The hidden Markov model
  • canonical forms / The hidden Markov model
  • notations / Notations
  • lambda model / The lambda model
  • design / Design
  • evaluation / Evaluation – CF-1
  • training / Training – CF-2
  • decoding / Decoding – CF-3
  • canonical forms, implementing / Putting it all together
  • training, test case 1 / Test case 1 – training
  • evaluation, test case 2 / Test case 2 – evaluation
  • as filtering technique / HMM as a filtering technique
  • performance consideration / Performance consideration
• Hidden Naïve Bayes (HNB) / Training
• hinge loss / The nonseparable case – the soft margin
• HMM constructor
  • config / Putting it all together
  • xt / Putting it all together
  • form / Putting it all together
  • quantize / Putting it all together
  • f / Putting it all together
• hyperplane / Binomial classification

I

• implementation, genetic algorithms
  • about / Implementation
  • software design / Software design
  • key components / Key components
  • selection operator / Selection
  • population growth, controlling / Controlling the population growth
  • GA configuration / The GA configuration
  • crossover operator / Crossover
  • mutation operator / Mutation
  • reproduction / Reproduction
  • solver / Solver
• implementation, Q-learning
  • about / Implementation
  • software design / Software design
  • states and actions / The states and actions
  • search space / The search space, The policy and action-value
  • Q-learning components / The Q-learning components
  • Q-learning training / The Q-learning training
  • tail recursion to rescue / Tail recursion to the rescue
  • validation / The validation
  • prediction / The prediction
• information retrieval and text mining
  • about / Basics of information retrieval
• input forward propagation, training epoch
  • about / Step 1 – input forward propagation
  • computational flow / The computational flow
  • error functions / Error functions
  • operating nodes / Operating modes
  • softmax / Softmax
• insensitive error
  • about / An overview

J

• Jacobian matrix
  • about / Jacobian and Hessian matrices
• Java
  • about / Java
• JBlas/Linpack
  • URL / Don't reinvent the wheel!
• JFreeChart
  • about / JFreeChart
  • description / Description
  • licensing / Licensing
  • installation / Installation
  • installation, for Mac OSX / Installation
  • installation, for Windows / Installation
• JFreeChart library
  • about / Bias-variance decomposition

K

• K-fold cross-validation scheme / Assessing a model
• K-means clustering
  • about / K-means clustering
  • similarity, measuring / Measuring similarity
  • algorithm, defining / Defining the algorithm
  • cluster configuration / Step 1 – cluster configuration
  • cluster assignment / Step 2 – cluster assignment
  • reconstruction/error minimization / Step 3 – reconstruction/error minimization
  • classification / Step 4 – classification
  • curse of dimensionality / The curse of dimensionality
  • evaluation, setting up / Setting up the evaluation
  • results, evaluating / Evaluating the results
  • number of clusters, tuning / Tuning the number of clusters
  • validation / Validation
• Kalman smoothing
  • about / Kalman smoothing
• kernel functions
  • about / Kernel functions, An overview
  • common discriminative kernels / Common discriminative kernels
  • linear kernel (dot product) / Common discriminative kernels
  • polynomial kernel / Common discriminative kernels
  • radial basis function (RBF) / Common discriminative kernels
  • sigmoid kernel / Common discriminative kernels
  • Laplacian kernel / Common discriminative kernels
  • log kernel / Common discriminative kernels
  • kernel monadic composition / Kernel monadic composition
• kernel trick
  • about / The kernel trick
• key components, genetic algorithm implementation
  • population / Population
  • chromosomes / Chromosomes
  • genes / Genes
• keyquality metrics
  • about / Key quality metrics

L

• L1 regularization / Ln roughness penalty
• L2 regularization / Ln roughness penalty
• Lagrange multipliers
  • about / Lagrange multipliers
• Laplace / The zero-frequency problem
• lasso regularization
  • about / Ln roughness penalty
• Latent Dirichlet allocation (LDA)
  • about / Probabilistic graphical models
• lazy methods
  • about / Computation on demand
• LDL decomposition / LDL decomposition
• learning classifier systems (LCS)
  • about / Learning classifier systems, Introduction to LCS
  • components / Introduction to LCS
  • features / Why LCS?
  • terminology / Terminology
  • benefits / Benefits and limitations of learning classifier systems
  • limitations / Benefits and limitations of learning classifier systems
• learning vector quantization / Clustering
• least squares problem / Numerical optimization
• lemmatization / Basics of information retrieval
• Levenberg-Marquardt
  • about / Levenberg-Marquardt
• Levenstein distance / Basics of information retrieval
• libraries
  • about / Other libraries and frameworks
• libraries directory
  • about / List of libraries and tools
• LIBSVM
  • about / LIBSVM
  • URL, for downloading / LIBSVM
  • URL, for documentation / LIBSVM
  • benefits / LIBSVM
• LIBSVM, Java classes
  • svm_model / LIBSVM
  • svm_node / LIBSVM
  • svm_parameters / LIBSVM
  • svm_problem / LIBSVM
  • svm / LIBSVM
• Lidstone / The zero-frequency problem
• likelihood
  • about / Formalism
• Limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) / L-BFGS
• linear algebra
  • about / Linear algebra
  • QR decomposition / QR decomposition
  • LU factorization / LU factorization
  • LDL decomposition / LDL decomposition
  • Cholesky factorization / Cholesky factorization
  • singular value decomposition (SVD) / Singular Value Decomposition
  • Eigenvalue decomposition / Eigenvalue decomposition
  • algebraic libraries / Algebraic and numerical libraries
  • numerical libraries / Algebraic and numerical libraries
• linear chain CRF / Introduction to CRF
• linear chain structured graph CRF / Introduction to CRF
• linear regression
  • about / Linear regression
  • one-variate linear regression / One-variate linear regression
  • ordinary least squares regression / Ordinary least squares regression
  • versus SVR / SVR versus linear regression
• linear SVM
  • about / The linear SVM
  • separable case (hard margin) / The separable case – the hard margin
  • nonseparable case (soft margin) / The nonseparable case – the soft margin
• LogBinRegression constructor
  • obsSet / Step 5 – implementing the classifier
  • expected / Step 5 – implementing the classifier
  • maxIters / Step 5 – implementing the classifier
  • eta / Step 5 – implementing the classifier
  • eps / Step 5 – implementing the classifier
• logistic regression
  • about / Logistic regression
  • logistic function / Logistic function
  • binomial classification / Binomial classification
  • design / Design
  • training workflow / The training workflow
  • classification / Classification
• low-band filter
  • about / The exponential moving average
• LU factorization
  • about / LU factorization
  • basic LU factorization / LU factorization
  • with pivot / LU factorization

M

• machine learning
  • features / Why machine learning?
• machine learning algorithms
  • taxonomy / Taxonomy of machine learning algorithms
• machine learning problems
  • classification / Classification
  • prediction / Prediction
  • optimization / Optimization
  • regression / Regression
• maintainability
  • about / Maintainability
• Markov decision processes
  • about / Markov decision processes
  • Markov property / Markov decision processes, The Markov property
  • first order discrete Markov chain / The first order discrete Markov chain
• master-workers, Akka
  • about / Master-workers
  • exchange of messages / Exchange of messages
  • worker actors / Worker actors
  • workflow controller / The workflow controller
  • master actor / The master actor
  • master with routing / Master with routing
  • discrete Fourier transform (DFT) / Distributed discrete Fourier transform
  • limitations / Limitations
• mathematical abstractions
  • about / Supporting mathematical abstractions
  • variable declaration / Step 1 – variable declaration
  • model definition / Step 2 – model definition
  • instantiation / Step 3 – instantiation
• mathematical concepts
  • about / Mathematics
  • linear algebra / Linear algebra
  • first order predicate logic / First order predicate logic
  • Jacobian matrix / Jacobian and Hessian matrices
  • Hessian matrix / Jacobian and Hessian matrices
  • optimization techniques / Summary of optimization techniques
  • dynamic programming / Overview of dynamic programming
• mathematical notation / Mathematical notation for the curious
• maximum margin classifiers
  • kernel trick / Max-margin classification
• mean squared error (MSE) / One-variate linear regression
• measurement noise covariance / The measurement equation
• message-passing mechanisms
  • fire-and-forget or tell / The Actor model
  • send-and-receive or ask / The Actor model
• metaphor for graphical models / Probabilistic graphical models
• methodology
  • defining / Defining a methodology
• Michigan approach / Why LCS?
• mixins
  • about / Composing mixins to build a workflow
• mixins, composing for building workflow
  • about / Composing mixins to build a workflow
  • problem, understanding / Understanding the problem
  • modules, defining / Defining modules
  • workflow, instantiating / Instantiating the workflow
• model
  • about / A model by any other name
  • features / A model by any other name
  • attributes / A model by any other name
  • variables / A model by any other name
  • parametric / A model by any other name
  • differential / A model by any other name
  • probabilistic / A model by any other name
  • graphical / A model by any other name
  • directed graphs / A model by any other name
  • numerical method / A model by any other name
  • chemistry / A model by any other name
  • taxonomy / A model by any other name
  • grammar and lexicon / A model by any other name
  • inference logic / A model by any other name
  • versus design / Model versus design
  • features, selecting / Selecting features
  • features, extracting / Extracting features
• model, assessing
  • about / Assessing a model
  • validation / Validation
  • cross-validation / Cross-validation
  • bias-variance decomposition / Bias-variance decomposition
  • overfitting / Overfitting
• model categorization
  • about / Model categorization
  • predictive models / Model categorization
  • descriptive models / Model categorization
  • adaptive modeling / Model categorization
• modeling
  • about / Modeling, Model versus design
• monadic composition
  • about / Monads
• monadic data transformation
  • about / Monadic data transformation
  • explicit model / Monadic data transformation, Explicit models
  • implicit model / Monadic data transformation, Implicit models
  • error handling / Error handling
• monads
  • about / Abstraction, Monads
• Monitor class
  • about / Monitor
• morphism / Error handling
• moving averages
  • about / Moving averages
  • simple moving average / The simple moving average
  • weighted moving average / The weighted moving average
  • exponential moving average / The exponential moving average
• multilayer perceptron
  • about / The multilayer perceptron
  • activation function / The activation function
  • network topology / The network topology
  • design / Design
  • UML class diagram / Design
  • configuration / Configuration
  • network components / Network components
  • model / The model
  • problem types (modes) / Problem types (modes)
  • online training, versus batch training / Online training versus batch training
  • training epoch / The training epoch
  • training and classification / Training and classification
• multinomial Naïve Bayes model
  • about / Introducing the multinomial Naïve Bayes
  • formalism / Formalism
  • frequentist perspective / The frequentist perspective
  • predictive model / The predictive model
  • zero-frequency problem / The zero-frequency problem
• Multivariate Bernoulli classification
  • about / The Multivariate Bernoulli classification
  • model / Model
  • implementation / Implementation
• mutation operator, genetic algorithm implementation
  • about / Mutation
  • population / Population
  • chromosomes / Chromosomes
  • genes / Genes

N

• n-grams / Basics of information retrieval
• natural language processing (NLP) / The feature functions model
• Naïve Bayes
  • applying, to text mining / Naïve Bayes and text mining
• Naïve Bayes algorithm
  • pros / Pros and cons
  • cons / Pros and cons
• Naïve Bayes classifiers
  • about / Naïve Bayes classifiers
  • multinomial Naïve Bayes / Introducing the multinomial Naïve Bayes
• Naïve Bayes classifiers implementation
  • about / Implementation
  • design / Design
  • training / Training
  • classification / Classification
  • F1 validation / F1 validation
  • feature extraction / Feature extraction
  • testing / Testing
• Naïve Bayes models
  • about / Probabilistic graphical models
  • mathematical notation / Formalism
• net profit margin
  • about / Fundamental analysis
• net sales
  • about / Fundamental analysis
• network components, multilayer perceptron
  • about / Network components
  • network topology / The network topology
  • input and hidden layers / Input and hidden layers
  • output layer / The output layer
  • synapses / Synapses
  • connections / Connections
  • initialization weights / The initialization weights
• non-linear models, dimension reduction
  • about / Non-linear models
  • kernel PCA / Kernel PCA
  • manifolds / Manifolds
• nonlinear least squares minimization
  • about / Nonlinear least squares minimization
  • Gauss-Newton / Gauss-Newton
  • Levenberg-Marquardt / Levenberg-Marquardt
• nonlinear SVM
  • about / The nonlinear SVM
  • max-margin classification / Max-margin classification
  • kernel trick / The kernel trick
• NP problems
  • categories / NP problems
  • about / NP problems
• Nu-SVM / The nonseparable case – the soft margin
• numerical optimization
  • about / Numerical optimization
  • Newton / Numerical optimization
  • Quasi-Newton / Numerical optimization

O

• observation
  • about / Extracting features
• one-class SVC
  • used, for anomaly detection / Anomaly detection with one-class SVC
• one-variate linear regression
  • about / One-variate linear regression
  • implementation / Implementation
  • test case / Test case
• online training / Online training versus batch training
• operating income
  • about / Fundamental analysis
• operating profit margin
  • about / Fundamental analysis
• optimal substructures
  • about / Overview of dynamic programming
• optimization techniques
  • about / Summary of optimization techniques
  • gradient descent methods / Steepest descent
  • Quasi-Newton algorithms / Quasi-Newton algorithms
  • nonlinear least squares minimization / Nonlinear least squares minimization
  • Lagrange multipliers / Lagrange multipliers
• OptionModel class / The OptionModel class
• OptionProperty class / The OptionProperty class
• options trading
  • about / Options trading
• option trading, with Q-learning
  • about / Option trading using Q-learning
  • OptionProperty class / The OptionProperty class, The OptionModel class
  • quantization / Quantization
• ordinary least squares regression
  • about / Ordinary least squares regression
  • design / Design
  • implementation / Implementation
  • trending, test case 1 / Test case 1 – trending
  • feature selection, test case 2 / Test case 2 – feature selection
• overfitting
  • about / Overfitting, The frequentist perspective
• overlapping substructures
  • about / Overview of dynamic programming
• overload operators
  • about / Overloading
  • += / Overloading
  • + / Overloading

P

• padding / Value encoding
• parallel collections, Scala
  • about / Processing a parallel collection
  • benchmark framework / The benchmark framework
  • performance evaluation / Performance evaluation
• Parallel Colt
  • URL / Don't reinvent the wheel!
• Partial Least Square Regression (PLSR) / Evaluation
• partially connected neural networks / The network topology
• pay-out ratio
  • about / Fundamental analysis
• penalized least squares regression / Ln roughness penalty
• performance considerations
  • about / Performance considerations
  • K-means / K-means
  • EM / EM
  • PCA / PCA
• performance evaluation, Spark
  • about / Performance evaluation
  • parameters, tuning / Tuning parameters
  • tests / Tests
  • performance considerations / Performance considerations
• Pittsburgh approach / Why LCS?
• Pool
  • about / Key components
• posterior probability
  • about / Formalism
• Predicted Residual Error Sum of Squares (PRESS) / Evaluation
• predictive model
  • about / The predictive model
• predictive models / Model categorization
• price/book value ratio (PB)
  • about / Fundamental analysis
• price/earnings ratio (PE)
  • about / Fundamental analysis
• price/sales ratio (PS)
  • about / Fundamental analysis
• price patterns
  • about / Price patterns
• Price to Earnings/Growth (PEG)
  • about / Fundamental analysis
• primal problem / The nonseparable case – the soft margin
• principal components analysis, dimension reduction
  • about / Principal components analysis
  • algorithm / Algorithm
  • implementation / Implementation
  • test case / Test case
  • evaluation / Evaluation
• probabilistic graphical models
  • about / Probabilistic graphical models
• probabilistic kernels
  • about / Common discriminative kernels
• probabilistic reasoning
  • about / Probabilistic graphical models
• propositional logic
  • about / First order predicate logic
• protein sequence annotation
  • about / An overview

Q

• Q-learning
  • about / A solution – Q-learning
  • Bellman optimality equations / The Bellman optimality equations
  • temporal difference, for model-free learning / Temporal difference for model-free learning
  • action-value iterative update / Action-value iterative update
  • implementation / Implementation
  • for option trading / Option trading using Q-learning
  • implementing / Putting it all together
  • evaluation / Evaluation
• QR decomposition / Ordinary least squares regression
• QStar class / The Viterbi algorithm
• quantization / Value encoding
• Quasi-Newton algorithms
  • about / Quasi-Newton algorithms
  • Broyden-Fletcher-Goldfarb-Shanno (BGFS) / BFGS
  • Limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) / L-BFGS

R

• real-world Bayesian network
  • example / Probabilistic graphical models
• recombination
  • about / Evolutionary computing
• reconstruction/error minimization, K-means clustering
  • about / Step 3 – reconstruction/error minimization
  • K-means components, creating / Creating K-means components
  • tail recursive implementation / Tail recursive implementation
  • iterative implementation / Iterative implementation
• recursive algorithm, discrete Kalman filter
  • about / The recursive algorithm
  • prediction phase / Prediction
  • correction / Correction
  • Kalman smoothing / Kalman smoothing
  • fixed lag smoothing / Fixed lag smoothing
  • experimentation / Experimentation
• regression model / Design
• regression weights
  • about / One-variate linear regression
• regularization
  • about / Regularization, Ln roughness penalty
  • Ln roughness penalty / Ln roughness penalty
  • ridge regression / Ridge regression
• reinforcement learning
  • about / Model categorization, Reinforcement learning
  • problem / The problem
  • Q-learning / A solution – Q-learning
  • terminologies / Terminology
  • value of a policy / Value of a policy
  • pros / Pros and cons of reinforcement learning
  • cons / Pros and cons of reinforcement learning
• reinforcement learning agent
  • overview architecture / Concepts
• reproducible kernel Hilbert spaces
  • about / Common discriminative kernels
• residuals mean square (RMS) / Step 5 – minimizing the sum of square errors
• resilient distributed dataset (RDD) / Apache Spark
  • transformation / Apache Spark
  • action / Apache Spark
• Resilient Distributed Datasets (RDD)
  • about / Computation on demand
• ridge regression
  • about / Ln roughness penalty, Ridge regression
  • design / Design
  • implementation / Implementation
  • test case / Test case
• Riemann metric
  • about / Kernel monadic composition

S

• Scala
  • about / Why Scala?, Scala, Scala
  • features / Why Scala?
  • abstraction / Abstraction
  • scalability / Scalability
  • configurability / Configurability
  • maintainability / Maintainability
  • computation / Computation on demand
  • time series / Time series in Scala
  • object creation / Object creation
  • streams / Streams
  • parallel collections / Parallel collections
• scalability
  • about / Scalability
• scalability, with Actors
  • about / Scalability with Actors
  • Actor model / The Actor model
  • partitioning / Partitioning
  • reactive programming / Beyond actors – reactive programming
• Scalable frameworks
  • about / An overview
• Scala plugin for Eclipse
  • reference / Scala
• Scala plugin for IntelljIDEA
  • reference / Scala
• Scala programming
  • about / Scala programming
  • libraries directory / List of libraries and tools
  • code snippets format / Code snippets format
  • encapsulation / Encapsulation
  • class constructor template / Class constructor template
  • companion objects, versus case classes / Companion objects versus case classes
  • enumerations, versus case classes / Enumerations versus case classes
  • overload operators / Overloading
  • design template, for classifiers / Design template for immutable classifiers
  • data extraction / Data extraction
  • financial data sources / Data sources
  • document extraction / Extraction of documents
  • DMatrix class / DMatrix class
  • Counter class / Counter
  • Monitor class / Monitor
• Scalaz
  • about / Abstraction
• semi-supervised learning
  • about / Semi-supervised learning
• Sequential Minimal Optimization (SMO) / The nonseparable case – the soft margin
  • about / LIBSVM
• short interest
  • about / Fundamental analysis
• short interest ratio
  • about / Fundamental analysis
• shrinkage
  • about / Ln roughness penalty
• Simple Build Tool (SBT)
  • about / Scala
• simple build tool (sbt) / Deploying Spark
• simple moving average
  • about / The simple moving average
• simple workflow
  • writing / Writing a simple workflow
  • problem, scoping / Step 1 – scoping the problem
  • data loading / Step 2 – loading data
  • data, preprocessing / Step 3 – preprocessing the data
  • immutable normalization / Immutable normalization
  • patterns, discovering / Step 4 – discovering patterns
  • data, analyzing / Analyzing data
  • data, plotting / Plotting data
  • classifier, implementing / Step 5 – implementing the classifier
  • optimizer, selecting / Selecting an optimizer
  • model, training / Training the model
  • observations, classifying / Classifying observations
  • model, evaluating / Step 6 – evaluating the model
• singular value decomposition / Ordinary least squares regression
• singular value decomposition (SVD) / PCA
  • about / Singular Value Decomposition
• smoothing factor for counters
  • about / The zero-frequency problem
• smoothing kernels
  • about / Common discriminative kernels
• soft margin / The nonseparable case – the soft margin
• source code
  • about / Source code
  • context, versus view bounds / Context versus view bounds
  • presentation / Presentation
  • primitive types / Primitive types
  • type conversions / Type conversions
  • implicit conversion / Type conversions
  • immutability / Immutability
  • Scala iterators, performance / Performance of Scala iterators
• Spark ecosystem
  • about / Apache Spark
• Sparkling Water
  • about / 0xdata Sparkling Water
• spectral density estimation
  • purpose / Fourier analysis
• stackable trait injection / Composing mixins to build a workflow
• state space estimation, discrete Kalman filter
  • about / The state space estimation
  • transition equation / The transition equation
  • measurement equation / The measurement equation
• steepest descent
  • about / Steepest descent
• stemming / Basics of information retrieval
• stimuli / The biological background
• stochastic gradient descent / Ordinary least squares regression
  • about / Stochastic gradient descent
• substructures
  • about / Overview of dynamic programming
• sum of squared errors (SSE) / One-variate linear regression
• supervised learning
  • about / Supervised learning
• supervised machine learning algorithms
  • about / Supervised learning
  • generative models / Generative models
  • discriminative models / Discriminative models
• support vector machines (SVMs)
  • about / Support vector machines
  • linear SVM / The linear SVM
  • nonlinear SVM / The nonlinear SVM
• SVC
  • about / Support vector classifiers – SVC
  • binary SVC / The binary SVC
  • one-class SVC / Anomaly detection with one-class SVC
• SVM
  • components / Design
  • configuration parameters / Configuration parameters
  • performance considerations / Performance considerations
• SVM dual problem
  • kernel trick / Max-margin classification
• SVMLight
  • about / LIBSVM
• SVR
  • about / Support vector regression
  • overview / An overview
  • versus linear regression / SVR versus linear regression

T

• tagging model / Basics of information retrieval
• TaskSupport
  • about / Processing a parallel collection
• taxonomy, machine learning algorithms
  • about / Taxonomy of machine learning algorithms
  • unsupervised learning / Unsupervised learning
  • supervised learning / Supervised learning
  • semi-supervised learning / Semi-supervised learning
  • reinforcement learning / Reinforcement learning
• technical analysis
  • about / Technical analysis
  • trading data / Trading data
  • trading signal and strategy / Trading signals and strategy
  • price patterns / Price patterns
• technical analysis, terminology
  • bearish or bearish position / Terminology
  • bullish or bullish position / Terminology
  • long position / Terminology
  • neutral position / Terminology
  • oscillator / Terminology
  • overbought / Terminology
  • oversold / Terminology
  • relative strength index (RSI) / Terminology
  • resistance / Terminology
  • short position / Terminology
  • support / Terminology
  • technical indicator / Terminology
  • trading range / Terminology
  • trading signal / Terminology
  • volatility / Terminology
• temporal difference
  • about / Temporal difference for model-free learning
• terminology, LCS
  • environment / Terminology
  • agent / Terminology
  • predicate / Terminology
  • compound predicate / Terminology
  • action / Terminology
  • rule / Terminology
  • classifier / Terminology
  • rule fitness or score / Terminology
  • sensors / Terminology
  • input data stream / Terminology
  • rule matching / Terminology
  • covering / Terminology
  • predictor / Terminology
• terminology, reinforcement learning
  • environment / Terminology
  • agent / Terminology
  • state / Terminology
  • goal / Terminology
  • absorbing state / Terminology
  • terminal state / Terminology
  • action / Terminology
  • policy / Terminology
  • best policy / Terminology
  • reward / Terminology
  • episode / Terminology
  • horizon / Terminology
• test case, evaluation
  • about / Test case
  • implementation / Implementation
  • evaluation of models / Evaluation of models
  • impact of the hidden layers' architecture / Impact of the hidden layers' architecture
• test case, trading strategy
  • about / A test case
  • trading strategies, creating / Creating trading strategies
  • optimizer, configuring / Configuring the optimizer
  • best trading strategy, finding / Finding the best trading strategy
• testing, Naïve Bayes
  • about / Testing
  • textual information, retrieving / Retrieving the textual information
  • text mining classifier, evaluating / Evaluating the text mining classifier
• tests, genetic algorithms
  • about / Tests
  • weighted score / The weighted score
  • unweighted score / The unweighted score
• text analytics, conditional random field (CRF)
  • about / Regularized CRFs and text analytics
  • feature functions model / The feature functions model
  • design / Design
  • implementation / Implementation
  • CRF classifier, configuring / Configuring the CRF classifier
  • CRF model, training / Training the CRF model
  • CRF model, applying / Applying the CRF model
  • tests / Tests
  • training convergence profile / The training convergence profile
  • impact, of size of training set / Impact of the size of the training set
  • impact, of L2 regularization factor / Impact of the L2 regularization factor
• text mining
  • about / Naïve Bayes and text mining
  • Naïve Bayes, applying to / Naïve Bayes and text mining
• text mining methodology
  • implementing / Implementation
  • documents, analyzing / Analyzing documents
  • frequency of relative terms, extracting / Extracting the frequency of relative terms
  • features, generating / Generating the features
• ThreadPoolTaskSupport
  • about / Processing a parallel collection
• time series, in Scala
  • about / Time series in Scala
  • types and operations / Types and operations
  • magnet pattern / The magnet pattern
  • transpose operator / The transpose operator
  • differential operator / The differential operator
  • lazy views / Lazy views
• tools
  • about / Tools and frameworks
• trading signal / Trading signals and strategy
• trading strategies
  • about / Definition of trading strategies
  • trading operators / Trading operators
  • cost function / The cost function
  • trading signals / Trading signals
  • trading strategies / Trading strategies
  • trading signal encoding / Trading signal encoding
• training, hidden Markov model (HMM)
  • about / Training – CF-2
  • Baum-Welch estimator / The Baum-Welch estimator (EM)
• training, Naïve Bayes classifiers implementation
  • about / Training
  • class likelihood / Class likelihood
  • binomial model / Binomial model
  • multinomial model / The multinomial model
  • classifier components / Classifier components
• training and classification, multilayer perceptron
  • about / Training and classification
  • regularization / Regularization
  • model generation / The model generation
  • Fast Fisher-Yates shuffle / The Fast Fisher-Yates shuffle
  • prediction / Prediction
  • model fitness / Model fitness
• training epoch, multilayer perceptron
  • about / The training epoch
  • input forward propagation / Step 1 – input forward propagation
  • error backpropagation / Step 2 – error backpropagation
  • exit condition / Step 3 – exit condition
  • implementing / Putting it all together
• training workflow, logistic regression
  • about / The training workflow
  • optimizer, configuring / Step 1 – configuring the optimizer
  • Jacobian matrix, computing / Step 2 – computing the Jacobian matrix
  • convergence of optimizer, managing / Step 3 – managing the convergence of the optimizer
  • least squares problem, defining / Step 4 – defining the least squares problem
  • sum of square errors, minimizing / Step 5 – minimizing the sum of square errors
  • binomial multivariate logistic regression, testing / Test
• trending / Test case 1 – trending
• two-step lag smoothing algorithm / Experimentation
• Typesafe Activator
  • URL / Akka

U

• unsupervised learning
  • about / Unsupervised learning
  • data clustering / Clustering
  • dimension reduction / Dimension reduction

V

• validation, model
  • about / Validation
  • key quality metrics / Key quality metrics
  • F-score for binomial classification / F-score for binomial classification
  • F-score for multinomial classification / F-score for multinomial classification
• variance-bias trade-off
  • about / Bias-variance decomposition
• vector quantization
  • about / Clustering
• view bounds / Context versus view bounds
• Viterbi algorithm
  • about / The Viterbi algorithm
  • psi / The Viterbi algorithm
  • qStar / The Viterbi algorithm
  • delta / The Viterbi algorithm
• ViterbiPath class / Putting it all together
• ViterbiPath object / Putting it all together

W

• weighted moving average
  • about / The weighted moving average
• WordNet / Basics of information retrieval
• workflow computational model
  • about / A workflow computational model
  • mathematical abstractions, supporting / Supporting mathematical abstractions
  • mixins, combining to build workflow / Composing mixins to build a workflow
  • modularization / Modularization

X

• 0xdata H2O / 0xdata Sparkling Water
• 0xdata Sparkling Water
  • about / 0xdata Sparkling Water

Y

• 1-year Treasury bill (1yTB)
  • about / Introducing the multinomial Naïve Bayes
• Yahoo Finances / Step 1 – scoping the problem
• YahooFinancials / Data sources

Z

• zero-frequency problem
  • about / The zero-frequency problem