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Scala for Machine Learning, Second Edition - Second Edition

You're reading from  Scala for Machine Learning, Second Edition - Second Edition

Product type Book
Published in Sep 2017
Publisher Packt
ISBN-13 9781787122383
Pages 740 pages
Edition 2nd Edition
Languages
Toc

Table of Contents (27) Chapters close

Scala for Machine Learning Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
1. Getting Started 2. Data Pipelines 3. Data Preprocessing 4. Unsupervised Learning 5. Dimension Reduction 6. Naïve Bayes Classifiers 7. Sequential Data Models 8. Monte Carlo Inference 9. Regression and Regularization 10. Multilayer Perceptron 11. Deep Learning 12. Kernel Models and SVM 13. Evolutionary Computing 14. Multiarmed Bandits 15. Reinforcement Learning 16. Parallelism in Scala and Akka 17. Apache Spark MLlib Basic Concepts References Index

Chapter 5


[5:1] CFCS: Entropy and Kullback-Leibler Divergence, M. Osborne - University of Edinburg 2008 - http://www.inf.ed.ac.uk/teaching/courses/cfcs1/lectures/entropy.pdf

[5:2] Jensen-Shannon divergence, Wikipedia the free encyclopedia Wikimedia Foundation - https://en.wikipedia.org/wiki/Jensen–Shannon_divergence

[5:3] Machine Learning: A Probabilistic Perspective §2.3.8 Mutual Information, K. Murphy – MIT Press 2012

[5:4] Learning with Bregman Divergences, I. Dhillon, J. Ghosh - University of Texas at Austin - http://www.cs.utexas.edu/users/inderjit/Talks/bregtut.pdf

[5:5] A Tutorial on Principal Components Analysis, L. Smith, - 2002 http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf

[5:6] Fast Cross-validation in Robust PCA, S. Engelen, M. Hubert - COMPSTAT 2004 symposium, Partial Least Squares Physica-Verlag/Springer –http://wis.kuleuven.be/stat/robust/papers/2004/fastcvpcaCOMPSTAT2004.pdf

[5:7] A survey of dimension reduction techniques, I. Fodor - Center for Applied Scientific Computing Lawrence Livermore National Laboratory 2002 - https://e-reports-ext.llnl.gov/pdf/240921.pdf

[5:8] Multiple Correspondence Analysis - Wikipedia

https://en.wikipedia.org/wiki/Multiple_correspondence_analysis

[5:9] Dimension Reduction for Fast Similarity Search in Large Time Series Databases. E. Keogh, K. Chakrabarti, M. Pazzani, S. Mehrotra. - Dept. of Information and Computer Science, University of California Irvine 2000 - http://www.ics.uci.edu/~pazzani/Publications/dimen.pdf

[5:10] Manifold learning with applications to object recognition, D Thompson - Carnegie-Mellon University Course AP 6 - https://www.cs.cmu.edu/~efros/courses/AP06/presentations/ThompsonDimensionalityReduction.pdf

[5:11] Manifold learning: Theory and Applications, Y. Ma, Y. Fu - CRC Press 2012

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