»úÆ÷ѧϰµÄ×ۺϻù´¡(Machine Learning¡ª¡ªA Comprehensive Foundation)

CHAPTER 1 INTRODUCTION 1 1.1 ABOUT LEARNING 1 1.2 LEARN FROM WHERE: DATA 2 1.3 WHAT TO GET FROM LEARNING: PATTERNS 3 1.4 HOW TO LEARN: SCHEMES 5 1.5 HOW TO EVALUATE: GENERALIZATION 9 1.6 LEARN FOR WHAT: ENGINEERINGS AND/OR SCIENCES 10 1.7 LEARN TO BE INTELLIGENT 14 1.8 SUMMARY 15 REFERENCES 16 CHAPTER 2 PERFORMANCE EVALUATION 17 2.1 EVALUATING A MODEL 17 2.2 COMPARISON TEST 22 2.3 BIASª²VARIANCE DECOMPOSITION AND SYSTEM DEBUGGING 24 2.4 CLUSTER VALIDITY INDICES 32 2.5 SUMMARY 33 REFERENCES 33 CHAPTER 3 REGRESSION ANALYSIS 35 3.1 REGRESSION PROBLEM 35 3.2 LINEAR REGRESSION 36 3.3 LOGISTIC REGRESSION 40 3.4 REGULARIZATION 43 3.5 SUMMARY 48 REFERENCES 49 CHAPTER 4 PERCEPTRON AND MULTILAYER PERCEPTRON 50 4.1 PERCEPTRON 50 4.2 MULTILAYER PERCEPTRON 59 4.3 MLP IN APPLICATIONS 66 4.4 SUMMARY 67 REFERENCES 68 CHAPTER 5 SUPPORT VECTOR MACHINES 70 5.1 LINEAR SUPPORT VECTOR MACHINE 70 5.2 NONLINEAR SUPPORT VECTOR MACHINE 75 5.3 SUPPORT VECTOR REGRESSION 76 5.4 MERITS AND LIMITATIONS 78 5.5 SUMMARY 80 REFERENCES 80 CHAPTER 6 UNSUPERVISED LEARNING 83 6.1 THE TASK OF CLUSTERING 83 6.2 SIMILARITY MEASURES 84 6.3 Kª²MEANS 91 6.4 SELFª²ORGANIZING MAP 94 6.5 SUMMARY 100 REFERENCES 100 Chapter7 REPRESENTATION LEARNING 103 7.1 PRINCIPAL COMPONENTS ANALYSIS(PCA) 104 7.2 LINEAR DISCRIMINANT ANALYSIS (LDA) 110 7.3 INDEPENDENT COMPONENT ANALYSIS (ICA) 113 7.4 NONª²NEGATIVE MATRIX FACTORIZATION (NMF) 119 7.5 SUMMARY 122 REFERENCES 123 CHAPTER 8 PROBLEM DECOMPOSITION 126 8.1 CODING AND DECODING 126 8.2 DISTRIBUTED OUTPUT CODE 129 8.3 ERRORª²CORRECTING OUTPUT CODE 130 8.4 SUMMARY 135 REFERENCES 136 CHAPTER 9 ENSEMBLE LEARNING 138 9.1 DESIGN OF A MULTIPLE CLASSIFIER SYSTEM 138 9.2 DESIGN OF CLASSIFIER ENSEMBLES 139 9.3 DESIGN OF COMBINATION RULES 142 9.4 AN MCS INSTANCE: PSOª²WCM 144 9.5 SUMMARY 148 REFERENCES 149 CHAPTER 10 CONVOLUTIONAL NEURAL NETWORK 151 10.1 WHY NOT A DEEP MLP 151 10.2 CONVOLUTION OPERATION 153 10.3 CONVOLUTIONAL NEURAL NETWORK 156 10.4 HYPER PARMAETERS 163 10.5 AN EXAMPLE 165 10.6 SUMMARY 167 REFERENCES 168 CHAPTER 11 ARTIFICIAL INTELLIGENCE AIDED MENINGITIS DIAGNOSTIC SYSTEM 170 11.1 DATA SET AND PREª²PROCESSING 170 11.2 LEARNING A DIAGNOSTIC MODEL 172 11.3 PERFORMANCE EVALUATION 174 REFERENCES 179 CHAPTER12 CHALLENGES AND OPPORTUNITIES 181 12.1 TODAY'S MACHINE LEARNING 181 12.2 CHALLENGES AND OPPORTUNITIES 183 REFERENCES 189