MACHINE LEARNING TECHNIQUES - LASA

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6 I. Introduction 1.1 What is Machine Learning? - Definitions Machine Learning is the field of scientific study that concentrates on induction algorithms and on other algorithms that can be said to “learn”. Machine Learning Journal, Kluwer Acadnemic Machine Learning is an area of artificial intelligence involving developing techniques to allow computers to “learn”. More specifically, machine learning is a method for creating computer programs by the analysis of data sets, rather than the intuition of engineers. Machine learning overlaps heavily with statistics, since both fields study the analysis of data. Webster Dictionary Machine learning is a branch of statistics and computer science, which studies algorithms and architectures that learn from data sets. WordIQ 1.1.1 ML Resources: http://www.machinelearning.org/index.html ML List http://www.ics.uci.edu/~mlearn/MLList.html ML Repository http://www.ics.uci.edu/~mlearn/MLRepository.html MLnet http://www.mlnet.org/ KDnet http://www.kdnet.org/control/index NIPS http://nips.cc/ Pascal http://www.pascal-network.org/ "Archives of the Machine Learning List" "This is a repository of databases, domain theories and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms." Machine Learning network online information service. "This site is dedicated to the field of machine learning, knowledge discovery, case-based reasoning, knowledge acquisition, and data mining." "The KDNet (= Knowledge Discovery Network of Excellence) is an open Network of participants from science, industry and the public sector. The major purpose of this international project is to integrate real-life business problems into research discussions and to collaborate in shaping the future of Knowledge Discovery and Data Mining." Neural Information Processing Conference – on-line repository of all research papers on theoretical ML Network of excellence on Pattern Recognition, Statistical Modelling and Computational Learning © A.G.Billard 2004 – Last Update March 2011
7 Journals: • Machine Learning • IEEE Transactions on Signal processing • IEEE Transactions on Pattern Analysis • IEEE Transactions on Pattern Recognition • The Journal of Machine Learning Research • Data Mining and Knowledge Discovery • ACM Transactions on Information Systems • Neural Networks • Neural Computation • Biological Cybernetics Conferences: • ICML: Int. Conf. on Machine Learning • NIPS: Neural Information Processing Conference • COLT: Annual Conference on Learning Theory • IJCAI: Int. Joint Conf. on Artificial Intelligence • ICANN: Int. Conf. on Artificial Neural Networks • ESANN: European Conf. on Artificial Neural Networks • IJCNN: Int. Joint Conf. on Neural Networks 1.2 What is Learning? A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E. Mitchell, Tom. M. 1997. Machine Learning. New York: McGraw-Hill. Things learn when they change their behavior in a way that makes them perform better in the future. Witten, Ian H., and Eibe Frank. 2000. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. San Diego, CA: Morgan Kaufmann. In this class, we will approach the problem by stating that: Learning consists of gradually finding a solution to a problem. © A.G.Billard 2004 – Last Update March 2011
Page 1 and 2: SCHOOL OF ENGINEERING MACHINE LEARN
Page 3 and 4: 3 4. 4 Regression Techniques ......
Page 5: 5 9.2.2 Probability Distributions,
Page 9 and 10: 9 Performance What would be an opti
Page 11 and 12: 11 1.2.3 Key features for a good le
Page 13 and 14: 13 1.3.2 Crossvalidation To ensure
Page 15 and 16: 15 In particular, we will consider
Page 17 and 18: 17 2.1 Principal Component Analysis
Page 19 and 20: 19 ( ) Xʹ′ = W X − µ (2.6) i
Page 21 and 22: 21 2.1.2.2 Reconstruction error min
Page 23 and 24: 23 PCA is an example of PP approach
Page 25 and 26: 25 Algorithm: If one further assume
Page 27 and 28: 27 The CCA algorithm consists thus
Page 29 and 30: 29 Figure 2-6: Mixture of variables
Page 31 and 32: 31 2.3.2 Why Gaussian variables are
Page 33 and 34: 33 • In our general definition of
Page 35 and 36: 35 2.3.5 ICA Ambiguities We cannot
Page 37 and 38: 37 Denote by g the derivative of th
Page 39 and 40: 39 3 Clustering and Classification
Page 41 and 42: 41 An agglomerative clustering star
Page 43 and 44: 43 3.1.1.1 The CURE Clustering Algo
Page 45 and 46: 45 Disadvantages of hierarchical cl
Page 47 and 48: 47 Cases where K-means might be vie
Page 49 and 50: 49 3.1.4 Clustering with Mixtures o
Page 51 and 52: 51 k ( σ j ) 2 = k ∑ i α = r k
Page 53 and 54: 53 Theα are the so-called mixing c
Page 55 and 56: 55 Figure 3-16: Clustering with 3 G
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57 When the transformation A is lin
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59 C: X → Y ( ) C x K = arg max
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61 Figure 3-18: Linear combination
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63 Figure 3-19: Bayes classificatio
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65 ⎛⎛ min ⎜⎜ w ⎝⎝ N i=
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67 T ( yi − xi w) 2 M ⎛⎛ ⎞
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69 Figure 4-2: Illustration of the
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71 4.4.2 Multi-Gaussian Case It is
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73 5 Kernel Methods These lecture n
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75 The kernel k provides a metric o
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77 M 1 T v = ∑ x ( x ) v M λ i j
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79 1 M The solutions to the dual ei
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81 5.4 Kernel CCA The linear versio
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83 additional ridge parameter induc
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85 Figure 5-3: TOP: Marginal (left)
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87 statistical independence. We def
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89 J j ( µ 1,...., µ K) = ∑∑
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91 A simple pattern recognition alg
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93 ( ) ( , ) f x = sign w x + b (5.
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95 Figure 5-6: A binary classificat
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97 where N is the number of support
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99 5.8 Support Vector Regression In
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101 The optimization problem given
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103 Note that since we never have t
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105 Figure 5-13: Effect of the kern
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107 To better understand the effect
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109 5.9 Gaussian Process Regression
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111 One can then use the above expr
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113 5.9.2 Equivalence of Gaussian P
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115 5.9.3 Curse of dimensionality,
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117 The weight w determines the slo
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119 Figure 5-21: Example of success
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121 • its performance tends to de
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123 neurons. Furthermore, they lear
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125 The sigmoid f x ( x) ( ) = tanh
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127 6.3.2 Information Theory and th
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129 ( R) ⎛⎛det ⎞⎞ I( x, y)
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131 y= ∑ w x ) of Because of the
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133 6.5 Willshaw net David Willshaw
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135 6.6.1 Weights bounds One of the
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137 Figure 6-11: The weight vector
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139 6.6.4 Oja’s one Neuron Model
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141 If y i and y are highly correla
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143 Foldiak’s second model allows
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145 ∂ ∂ J 1 = fy − 1 2 λ 1yf
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147 6.8 The Self-Organizing Map (SO
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149 6. Decrease the size of the nei
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151 the resulting distribution is a
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153 To simplify the description of
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155 C µν −1 where ( ) is the µ
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157 The continuous time Hopfield ne
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159 ∂f If the slope is negative,
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161 7.2 Hidden Markov Models Hidden
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163 Figure 7-2: Schematic illustrat
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165 these two quantities to compute
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167 7.2.4 Decoding an HMM There are
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169 7.2.5 Further Readings Rabiner,
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171 7.3.1 Principle In reinforcemen
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173 general, acting to maximize imm
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175 of reinforcement learning makes
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177 8 Genetic Algorithms We conclud
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179 However, you must define geneti
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181 Often the crossover operator an
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183 ( A λI) x 0 − = (8.5) where
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185 Joint probability: The joint pr
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187 The two most classical distribu
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189 9.2.7 Statistical Independence
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191 1 1 1 1 h x ∫ a b a b 0 a a
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193 9.4 Estimators 9.4.1 Gradient d
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195 9.4.2.1 Maximum Likelihood Mach
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197 10 References • Machine Learn
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