Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
All Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletter Hub
Free Learning
Arrow right icon
timer SALE ENDS IN
0 Days
:
00 Hours
:
00 Minutes
:
00 Seconds
Arrow up icon
GO TO TOP
Mastering Java Machine Learning

You're reading from   Mastering Java Machine Learning A Java developer's guide to implementing machine learning and big data architectures

Arrow left icon
Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781785880513
Length 556 pages
Edition 1st Edition
Languages
Concepts
Arrow right icon
Authors (2):
Arrow left icon
 Kamath Kamath
Author Profile Icon Kamath
Kamath
Krishna Choppella Krishna Choppella
Author Profile Icon Krishna Choppella
Krishna Choppella
Arrow right icon
View More author details
Toc

Table of Contents (20) Chapters Close

Mastering Java Machine Learning
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
1. Machine Learning Review 2. Practical Approach to Real-World Supervised Learning FREE CHAPTER 3. Unsupervised Machine Learning Techniques 4. Semi-Supervised and Active Learning 5. Real-Time Stream Machine Learning 6. Probabilistic Graph Modeling 7. Deep Learning 8. Text Mining and Natural Language Processing 9. Big Data Machine Learning – The Final Frontier Linear Algebra Probability Index

References


  1. K. Pearson (1901). On lines and planes of closest fit to systems of points in space. Philosophical Magazine, 2:559–572.

  2. A. D. Back (1997). "A first application of independent component analysis to extracting structure from stock returns," Neural Systems, vol. 8, no. 4, pp. 473–484.

  3. Tipping ME, Bishop CM (1999). Probabilistic principal component analysis. Journal of the Royal Statistical Society, Series B, 61(3):611–622. 10.1111/1467-9868.00196

  4. Sanjoy Dasgupta (2000). Experiments with random projection. In Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence (UAI'00), Craig Boutilier and Moisés Goldszmidt (Eds.). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 143-151.

  5. T. Cox and M. Cox (2001). Multidimensional Scaling. Chapman Hall, Boca Raton, 2nd edition.

  6. Bernhard Schoelkopf, Alexander J. Smola, and Klaus-Robert Mueller (1999). Kernel principal component analysis. In Advances in kernel methods, MIT Press, Cambridge, MA, USA 327-352.

  7. Tenenbaum...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at £13.99/month. Cancel anytime
Visually different images