Machine learning

Machine Learning, a vital and core area of artificial intelligence (AI), is propelling the AI field ever further and making it one of the most compelling areas of computer science research. This textbook offers a comprehensive and unbiased introduction to almost all aspects of machine learning, from...

Full description

Saved in:
Bibliographic Details
Main Authors Zhou, Zhi-Hua (Computer scientist), Liu, Shaowu
Format eBook Book
LanguageEnglish
Published Singapore Springer 2021
Springer Singapore
Edition1
Subjects
Computer Science
Data Mining and Knowledge Discovery
Machine Learning
Mathematics of Computing
Online AccessGet full text
ISBN9811519668
9789811519666
DOI10.1007/978-981-15-1967-3

Cover

Table of Contents:
  • 8.4 Combination Strategies -- 8.5 Diversity -- 8.6 Further Reading -- Exercises -- Break Time -- References -- 9 Clustering -- 9.1 Clustering Problem -- 9.2 Performance Measure -- 9.3 Distance Calculation -- 9.4 Prototype Clustering -- 9.5 Density Clustering -- 9.6 Hierarchical Clustering -- 9.7 Further Reading -- Exercises -- Break Time -- References -- 10 Dimensionality Reduction and Metric Learning -- 10.1 k-Nearest Neighbor Learning -- 10.2 Low-Dimensional Embedding -- 10.3 Principal Component Analysis -- 10.4 Kernelized PCA -- 10.5 Manifold Learning -- 10.6 Metric Learning -- 10.7 Further Reading -- Exercises -- Break Time -- References -- 11 Feature Selection and Sparse Learning -- 11.1 Subset Search and Evaluation -- 11.2 Filter Methods -- 11.3 Wrapper Methods -- 11.4 Embedded Methods and L1 Regularization -- 11.5 Sparse Representation and Dictionary Learning -- 11.6 Compressed Sensing -- 11.7 Further Reading -- Exercises -- Break Time -- References -- 12 Computational Learning Theory -- 12.1 Basic Knowledge -- 12.2 PAC Learning -- 12.3 Finite Hypothesis Space -- 12.4 VC Dimension -- 12.5 Rademacher Complexity -- 12.6 Stability -- 12.7 Further Reading -- Exercises -- Break Time -- References -- 13 Semi-Supervised Learning -- 13.1 Unlabeled Samples -- 13.2 Generative Methods -- 13.3 Semi-Supervised SVM -- 13.4 Graph-Based Semi-Supervised Learning -- 13.5 Disagreement-Based Methods -- 13.6 Semi-Supervised Clustering -- 13.7 Further Reading -- Exercises -- Break Time -- References -- 14 Probabilistic Graphical Models -- 14.1 Hidden Markov Model -- 14.2 Markov Random Field -- 14.3 Conditional Random Field -- 14.4 Learning and Inference -- 14.5 Approximate Inference -- 14.6 Topic Model -- 14.7 Further Reading -- Exercises -- Break Time -- References -- 15 Rule Learning -- 15.1 Basic Concepts -- 15.2 Sequential Covering -- 15.3 Pruning Optimization
  • Intro -- Preface -- Contents -- Symbols -- 1 Introduction -- 1.1 Introduction -- 1.2 Terminology -- 1.3 Hypothesis Space -- 1.4 Inductive Bias -- 1.5 Brief History -- 1.6 Application Status -- 1.7 Further Reading -- Exercises -- Break Time -- References -- 2 Model Selection and Evaluation -- 2.1 Empirical Error and Overfitting -- 2.2 Evaluation Methods -- 2.3 Performance Measure -- 2.4 Comparison Test -- 2.5 Bias and Variance -- 2.6 Further Reading -- Exercises -- Break Time -- References -- 3 Linear Models -- 3.1 Basic Form -- 3.2 Linear Regression -- 3.3 Logistic Regression -- 3.4 Linear Discriminant Analysis -- 3.5 Multiclass Classification -- 3.6 Class Imbalance Problem -- 3.7 Further Reading -- Exercises -- Break Time -- References -- 4 Decision Trees -- 4.1 Basic Process -- 4.2 Split Selection -- 4.3 Pruning -- 4.4 Continuous and Missing Values -- 4.5 Multivariate Decision Trees -- 4.6 Further Reading -- Exercises -- Break Time -- References -- 5 Neural Networks -- 5.1 Neuron Model -- 5.2 Perceptron and Multi-layer Network -- 5.3 Error Backpropagation Algorithm -- 5.4 Global Minimum and Local Minimum -- 5.5 Other Common Neural Networks -- 5.6 Deep Learning -- 5.7 Further Reading -- Exercises -- Break Time -- References -- 6 Support Vector Machine -- 6.1 Margin and Support Vector -- 6.2 Dual Problem -- 6.3 Kernel Function -- 6.4 Soft Margin and Regularization -- 6.5 Support Vector Regression -- 6.6 Kernel Methods -- 6.7 Further Reading -- Exercises -- Break Time -- References -- 7 Bayes Classifiers -- 7.1 Bayesian Decision Theory -- 7.2 Maximum Likelihood Estimation -- 7.3 Naïve Bayes Classifier -- 7.4 Semi-Naïve Bayes Classifier -- 7.5 Bayesian Network -- 7.6 EM Algorithm -- 7.7 Further Reading -- Exercises -- Break Time -- References -- 8 Ensemble Learning -- 8.1 Individual and Ensemble -- 8.2 Boosting -- 8.3 Bagging and Random Forest
  • 15.4 First-Order Rule Learning -- 15.5 Inductive Logic Programming -- 15.6 Further Reading -- Exercises -- Break Time -- References -- 16 Reinforcement Learning -- 16.1 Task and Reward -- 16.2 K-Armed Bandit -- 16.3 Model-Based Learning -- 16.4 Model-Free Learning -- 16.5 Value Function Approximation -- 16.6 Imitation Learning -- 16.7 Further Reading -- Exercises -- Break Time -- References -- Appendix A Matrix -- A.1 Basic Operations -- A.2 Derivative -- A.3 Singular Value Decomposition -- Appendix B Optimization -- B.1 Lagrange Multiplier Method -- B.2 Quadratic Programming -- B.3 Semidefinite Programming -- B.4 Gradient Descent Method -- B.5 Coordinate Descent Method -- Appendix C Probability Distributions -- C.1 Common Probability Distributions -- C.2 Conjugate Distribution -- C.3 Kullback-Leibler Divergence -- Index