Chapter 4
[4:1] Unsupervised Learning, P. Dayan - The MIT Encyclopedia of the Cognitive Sciences, Wilson & Kiel editors 1998 - http://www.gatsby.ucl.ac.uk/~dayan/papers/dun99b.pdf
[4:2] Learning Vector Quantization (LVQ): Introduction to Neural Computation, J. Bullinaria – 2007 - http://www.cs.bham.ac.uk/~pxt/NC/lvq_jb.pdf
[4:3] The Elements of Statistical Learning: Data Mining, Inference and Prediction §14.3 Cluster Analysis, T. Hastie, R. Tibshirani, J. Friedman - Springer 2001
[4:4] Efficient and Fast Initialization Algorithm for K-means Clustering, International Journal of Intelligent Systems and Applications – M. Agha, W. Ashour - Islamic University of Gaza 2012 - http://www.mecs-press.org/ijisa/ijisa-v4-n1/IJISA-V4-N1-3.pdf
[4:5] A Comparative Study of Efficient Initialization Methods for the K-Means Clustering Algorithms, M E. Celebi, H. Kingravi, P Vela – 2012 - http://arxiv.org/pdf/1209.1960v1.pdf
[4:6] Machine Learning: A Probabilistic Perspective: §25.1 Clustering Introduction, K. Murphy – MIT Press 2012
[4:7] Maximum Likelihood from Incomplete Data via the EM Algorithm - Journal of the Royal Statistical Society Vo. 39 No .1 A. P. Dempster, N. M. Laird, and D. B. Rubin. 1977 - http://web.mit.edu/6.435/www/Dempster77.pdf
[4:8] Machine Learning: A Probabilistic Perspective §11.4 EM algorithm, K. Murphy – MIT Press 2012
[4:9] The Expectation Maximization Algorithm A short tutorial, S. Borman – 2009 - http://www.seanborman.com/publications/EM_algorithm.pdf
[4:10] Apache Commons Math library 3.3: org.apache.commons.math3.distribution.fitting, The Apache Software Foundation - http://commons.apache.org/proper/commons-math/javadocs/api-3.6/index.html
[4:11] Pattern Recognition and Machine Learning §9.3.2 An Alternative View of EM- Relation to K-means, C. Bishop –Springer 2006
[4:12] Machine Learning: A Probabilistic Perspective §11.4.8 Online EM, K. Murphy – MIT Press 2012
[4:13] Function approximation – Wikipedia the free encyclopedia Wikimedia Foundation - https://en.wikipedia.org/wiki/Function_approximation
[4:14] Function Approximation with Neural Networks and Local Methods: Bias, Variance and Smoothness, S. Lawrence, A.Chung Tsoi, A. Back - University of Queensland Australia 1998 - http://machine-learning.martinsewell.com/ann/LaTB96.pdf
