MACHINE LEARNING TECHNIQUES - LASA

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122 the visual system, where the first 10 or 11 processing stages have been identified. We distinguish feedforward projections that go from earlier processing stages (near the sensory input) to later ones (near the motor output), from feedback connections that go in the opposite direction. In addition to these long-range connections, neurons also link up with many thousands of their neighbours. In this way they form very dense, complex local networks. 6.2.3 Neurons and Synapses The basic computational unit in the nervous system is the nerve cell, or neuron. A neuron has: • Dendrites (inputs) • Cell body • Axon (output) A neuron receives input from other neurons (typically many thousands). Once the input exceeds a critical level, the neuron discharges a spike - an electrical pulse that travels from the body, down the axon, to the next neuron(s) (or other receptors). This spiking event is also called depolarization, and is followed by a refractory period, during which the neuron is unable to fire. The axon endings (Output Zone) almost touch the dendrites or cell body of the next neuron. Transmission of an electrical signal from one neuron to the next is effected by neurotransmitters, chemicals which are released from the first neuron and which bind to receptors in the second. This link is called a synapse. The extent to which the signal from one neuron is passed on to the next depends on many factors, e.g. the amount of neurotransmittor available, the number and arrangement of receptors, amount of neurotransmittor reabsorbed, etc. 6.2.4 Synaptic Learning Brains learn. From what we know of neuronal structures, one way brains learn is by altering the strengths of connections between neurons, and by adding or deleting connections between © A.G.Billard 2004 – Last Update March 2011
123 neurons. Furthermore, they learn "on-line", based on experience, and typically without the benefit of a benevolent teacher. The efficacy of a synapse can change as a result of experience, providing both memory and learning through long-term potentiation. One way this happens is through release of more neurotransmitter. Many other changes may also be involved. Long-term Potentiation: An enduring (>1 hour) increase in synaptic efficacy that results from high-frequency stimulation of an afferent (input) pathway à Hebbian Learning, see Section 6.4. 6.2.5 Summary The following properties of nervous systems are of particular interest in neurally-inspired models: • parallel, distributed information processing • high degree of connectivity among basic units • connections are modifiable based on experience • learning is a constant process, and usually unsupervised • learning is based only on local information • performance degrades gracefully if some units are removed © 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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69 Figure 4-2: Illustration of the
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Page 75 and 76: 75 The kernel k provides a metric o
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Page 81 and 82: 81 5.4 Kernel CCA The linear versio
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Page 85 and 86: 85 Figure 5-3: TOP: Marginal (left)
Page 87 and 88: 87 statistical independence. We def
Page 89 and 90: 89 J j ( µ 1,...., µ K) = ∑∑
Page 91 and 92: 91 A simple pattern recognition alg
Page 93 and 94: 93 ( ) ( , ) f x = sign w x + b (5.
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Page 111 and 112: 111 One can then use the above expr
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Page 115 and 116: 115 5.9.3 Curse of dimensionality,
Page 117 and 118: 117 The weight w determines the slo
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Page 125 and 126: 125 The sigmoid f x ( x) ( ) = tanh
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Page 133 and 134: 133 6.5 Willshaw net David Willshaw
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Page 137 and 138: 137 Figure 6-11: The weight vector
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Page 169 and 170: 169 7.2.5 Further Readings Rabiner,
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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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MACHINE LEARNING TECHNIQUES - LASA

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