MACHINE LEARNING TECHNIQUES - LASA

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120 6 Artificial Neural Networks Artificial neural network (ANN) has become one of those buzzwords that is either popular or unpopular, but leaves no one indifferent. Unfortunately, for most people, the word relates only to one type of neural network, namely feed-forward neural networks. ANNs are, however, far more than this. We will see, in this chapter and the next one, different ANN architectures that greatly differ from one another, both in terms of computation they can perform as well as in their algorithms. We will see how the architecture and the learning rule determine the type of computation an ANN can perform. 6.1 Applications of ANN ANNs can perform diverse types of computation. Here is a non-exhaustive list: Type of Computation Pattern recognition Associative memory PCA ICA Dimensionality Reduction Learning Time series ANNs Feed-forward ANN and Perceptron Hebbian Network, Willshaw Net, Hopfield Net Hebbian Network Anti-hebbian learning SOM, Kohonen TDNN, RNN ANNs are powerful tools that can deal with large, multidimensional, non-linear, datasets. They have found application in numerous domains, as statistical tools for data mining in finance and particle physics or as optimization tools in industrial control systems and robotics. There are two modes of functioning for an ANN: 1. Activation transfer mode when activation is transmitted throughout the network. 2. Learning mode when the network organizes itself, usually on the basis of the most recent activation transfer. 6.2 Biological motivation ANNs were developed with the goal to mimic the highly parallel and distributed computation performed in the human brain. 6.2.1 The Brain as an Information Processing System The human brain contains about 10 billion nerve cells, or neurons. On average, each neuron is connected to other neurons through about 10 000 synapses. (The actual figures vary greatly, depending on the local neuroanatomy.) The brain's network of neurons forms a massively parallel information processing system. This contrasts with conventional computers, in which a single processor executes a single series of instructions. Against this, consider the time taken for each elementary operation: neurons typically operate at a maximum rate of about 100 Hz, while a conventional CPU carries out several hundred million machine level operations per second. Despite of being built with very slow hardware, the brain has quite remarkable capabilities: © A.G.Billard 2004 – Last Update March 2011
121 • its performance tends to degrade gracefully under partial damage. In contrast, most programs and engineered systems are brittle: if you remove some arbitrary parts, very likely the whole will cease to function. • it can learn (reorganize itself) from experience. • this means that partial recovery from damage is possible if healthy units can learn to take over the functions previously carried out by the damaged areas. • it performs massively parallel computations extremely efficiently. For example, complex visual perception occurs within less than 100 ms, that is, 10 processing steps! • it supports our intelligence and self-awareness. (Nobody knows yet how this occurs.) processing elements element size energy use processing speed style of computation fault tolerant learns 10 14 synapses 10 -6 m 30 W 100 Hz parallel, distributed yes yes 10 8 transistors 10 -6 m 30 W (CPU) 10 9 Hz serial, centralized no a little As a discipline of Artificial Intelligence, Neural Networks attempt to bring computers a little closer to the brain's capabilities by imitating certain aspects of information processing in the brain, in a highly simplified way. 6.2.2 Neural Networks in the Brain The brain is not homogeneous. At the largest anatomical scale, we distinguish cortex, midbrain, brainstem, and cerebellum. Each of these can be hierarchically subdivided into many regions, and areas within each region, either according to the anatomical structure of the neural networks within it, or according to the function performed by them. The overall pattern of projections (bundles of neural connections) between areas is extremely complex, and only partially known. The best mapped (and largest) system in the human brain is © A.G.Billard 2004 – Last Update March 2011
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SCHOOL OF ENGINEERING MACHINE LEARN
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3 4. 4 Regression Techniques ......
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5 9.2.2 Probability Distributions,
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7 Journals: • Machine Learning
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9 Performance What would be an opti
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11 1.2.3 Key features for a good le
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13 1.3.2 Crossvalidation To ensure
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15 In particular, we will consider
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17 2.1 Principal Component Analysis
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19 ( ) Xʹ′ = W X − µ (2.6) i
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21 2.1.2.2 Reconstruction error min
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23 PCA is an example of PP approach
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25 Algorithm: If one further assume
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27 The CCA algorithm consists thus
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29 Figure 2-6: Mixture of variables
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31 2.3.2 Why Gaussian variables are
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33 • In our general definition of
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35 2.3.5 ICA Ambiguities We cannot
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37 Denote by g the derivative of th
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39 3 Clustering and Classification
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41 An agglomerative clustering star
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43 3.1.1.1 The CURE Clustering Algo
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45 Disadvantages of hierarchical cl
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47 Cases where K-means might be vie
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49 3.1.4 Clustering with Mixtures o
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51 k ( σ j ) 2 = k ∑ i α = r k
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53 Theα are the so-called mixing c
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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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Page 91 and 92: 91 A simple pattern recognition alg
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Page 169 and 170: 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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