Machine.Learning.An.Algorithmic.Perspective.2nd.Edition.1466583282

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Title: Machine Learning: An Algorithmic Perspective, 2nd Edition Author: Stephen Marsland Length: 457 pages Edition: 2 Language: English Publisher: Chapman and Hall/CRC Publication Date: 2014-10-08 ISBN-10: 1466583282 ISBN-13: 9781466583283 A Proven, Hands-On Approach for Students without a Strong
Chapman hall cro Machine learning Pattern Recognition Series SERIES EDITORS Ralf herbrich Thore gl Amazon development Center Microsoft research ltd Berlin, Germany Cambridge, UK AIMS AND SCOPE This series reflects the latest advances and applications in machine learning and pattern recog nition through the publication of a broad range of reference works, textbooks, and handbooks The inclusion of concrete examples, applications, and methods is highly encouraged. The scope of the series includes, but is not limited to, titles in the areas of machine learning, pattern rec- ognition, computational intelligence, robotics, computational/statistical learning theory, natural language processing, computer vision, game Al, game theory, neural networks, computational neuroscience, and other relevant topics, such as machine learning applied to bioinformatics or cognitive science, which might be proposed by potential contributors PUBLISHED TITLES BAYESIAN PROGRAMMING Pierre bessiere Emmanuel Mazer Juan-Manuel ahuactzin and Kamel Mekhnacha UTILITY-BASED LEARNING FROM DATA Craig Friedman and Sven Sandow HANDBOOK OF NATURAL LANGUAGE PROCESSING. SECOND EDITION Nitin indurkhva and fred. damerau COST-SENSITIVE MACHINE LEARNING Balaji Krishnapuram, Shipeng Yu, and Bharat Rao COMPUTATIONAL TRUST MODELS AND MACHINE LEARNING Xin Liu, Anwitaman Datta, and Ee-Peng lim MULTILINEAR SUBSPACE LEARNING: DIMENSIONALITY REDUCTION OF MULTIDIMENSIONAL DATA Haiping Lu, Konstantinos N Plataniotis, and Anastasios N venetsanopoulos MACHINE LEARNING: An Algorithmic Perspective, Second edition Stephen marsland A FIRST COURSE IN MACHINE LEARNING Simon rogers and Mark girolami MULTI-LABEL DIMENSIONALITY REDUCTION Liang Sun, Shuiwang i, and Jieping Ye ENSEMBLE METHODS: FOUNDATIONS AND ALGORITHMS Zhi-Hua zhou K18981 FM indd 2 8/26/1412:45PM Chapman hall crc Machine Learning Pattern Recognition Series MACHINE LEARNING An algorithmic perspective SECOND EDITION STEPHEN MARSLAND (CRC)CRC Press Taylor francis grot Boca raton London New York CRC Press is of the Taylor Francis Group, an informa business A ChaPMaN hall book K18981 FMindd 3 8/26/1412:45PM CRC Press Taylor Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca raton Fl 33487-2742 o 2015 by Taylor Francis Group, LLC CRC Press is an imprint of Taylor Francis group, an Informa business No claim to original U.S. Government works Version date: 20140826 International Standard Book Number-13: 978-1-4665-8333-7(eBook-PDF) This book contains information obtained from authentic and highly regarded sources. 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For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe Visit the Taylor francis Web site at http://www.taylorandfrancis.com and the crc press Web site at http://www.crcpress.com Again, for Monika Contents Prologue to 2nd Edition Prologue to lst Edit XIX chapter 1- Introduction 1.1 F DATA HAD MASS. THE EARTH WOULD BE A BLACK HOLE 1.2 LEARNING 1.2.1 Machine Learning 1.3 TYPES OF MACHINE LEARNING 1.4 SUPERVISED LEARNING 1.4.1 Regression 1445668 1. 4.2 Classification 1.5 THE MACHINE LEARNING PROCESS 1.6 A NOTE ON PROGRAMMING 11 1. A ROADMAP TO THE BOOK FURTHER READING chaPter 2. Preliminaries 2.1 SOME TERMINOLOGY 2. 1.1 Weight Space 16 2.1.2 The Curse of dimensionality 17 2.2 KNOWING WHAT YOU KNOW: TESTING MACHINE LEARNING AL- GORITHMS 19 2.2.1 Overfitting 19 2.2.2 Training, Testing, and Validation Scts 0 2.2.3 The Confusion matrix 21 2. 2. 4 Accuracy Metrics ) 2.2.5 The Receiver Operator Characteristic(ROC)Curve 24 2.2. 6 Unbalanced Datasets 2.2.7 Measurement precision 25 2.3 TURNING DATA INTO PROBABILITIES 27 2.3.1 Minimising Risk 30 ontents 2.3.2 The naive Ba Classific 2.4 SOME BASIC STATISTICS 32 2.4.1Av 2.4.2 Variance and covariance 32 2.4.3 The Gaussian 34 2.5 THE BIAS-VARIANCE TRADEOFF 35 FURTHER READING 36 PRACTICE QUES TIONS 37 chapter 3 Neurons, Neural Networks. and Linear discriminants 39 3.1 THE BRAIN AND THE NEURON 39 3.1.1Hebb’ s Rule 3.1.2 McCulloch and Pitts Neurons 3.1.3 Limitations of the Mc Culloch and Pitts Neuronal Model 3.2 NEURAL NETWORKS 43 3.3 THE PERCEPTRON 43 3.3.1 The Learning Rate n 3.3.2 The Bias Input 3. 3.3 The Perceptron Learning Algorithm 3.3.4 An Example of Perceptron Learning: Logic Functions 3.3.5 Implementation 49 3.4 LINEAR SEPARABILITY 55 3.4.1 The Perceptron Convergence Theorem 57 3.4.2 The Exclusive Or(XOR) Function 3.4.3 A Useful Insight 59 3.4.4 Another Example: The Pima Indian Dataset 61 3.4.5 Preprocessing Data Preparation 3.5 LINEAR REGRESSION 64 3.5.1 Linear Regression Examples FURTHER READING PRACTICE QUES TIONS CHAPTeR 4 The multi-layer Perceptron 4.1 GOING FORWARDS 4.1.1 Biases 73 4.2 GOING BACKWARDS BACK-PROPAGATION OF ERROR 74 4.2.1 The Multi-layer Perceptron algorithm 77 1.2. 2 Initialising the Weights 4.2.3 Different Output Activation Functions 81 Contents 1.2.1 Sequential and Batch Training 4.2.5 Local minima 4.2.6 Picking Up Momentum 84 4.2.7 Minibatches and stochastic gradient descent. 4.2.8 Other Improvements 4.3 THE MULTI-LAYER PERCEPTRON IN PRACTICE 85 4.3.1 Amount of Training Data 4.3.2 Number of Hidden layers 4.3.3 When to Stop Learning 4.4 EXAMPLES OF USING THE MLP 4.4.1 A Regression problem 4.4.2 Classification with the MLP 92 4.4.3 A Classification Example: The Iris Dataset 4.4.4 Time-Series Prediction 5 4.4.5 Data Compression: The Auto-Associativc Nctwork 97 4.5 A RECIPE FOR USING THE MLP 100 4.6 DERIVING BACK-PROPAGATION 101 4.6.1 The Network Output and the Error 101 4.6.2 The error of the network 102 4.6.3 Requirements of an Activation Function 103 4.6.4 Back-Propagation of error 104 4.6.5 The Output Activation Functions 107 4.6.6 An Alternative Error Function 108 FURTHER READING 108 PRACTICE QUESTIONS 109 CHAPTER 5. Radial Basis Functions and splines 5.1 RECEPTIVE FIELDS 5.2 THE RADIAL BASIS FUNCTION(RBF)NETWORK 5.2.1 Training thc rbF nctwork 117 5.3 INTERPOLATION AND BASIS FUNCTIONS 119 5.3.1 Bases and Basis Expansion 122 5.3.2 The Cubic spline 5.3.3 Fitting the spline to the data 123 5.3.4 SMoothing Splines 124 5.3.5 Iligher dimensions 125 5.3.6 Beyond the Bounds 127 FURTHER READING 127 PRACTICE QUES TIONS 128 【实例截图】
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