Machine Learning Foundations: Supervised, Unsupervised, and Advanced Learning

Preface
	Part I: Foundation
	Part II: Supervised Learning
	Part III: Unsupervised Learning
	Part IV: Advanced Topics
Contents
Part I Foundation
	1 Introduction
		1.1 Definition of Machine Learning
		1.2 Application Areas
			1.2.1 Classification
			1.2.2 Regression
			1.2.3 Clustering
			1.2.4 Hybrid Tasks
		1.3 Machine Learning Types
			1.3.1 Supervised Learning
			1.3.2 Unsupervised Learning
			1.3.3 Semi-supervised Learning
			1.3.4 Reinforcement Learning
		1.4 Related Areas
			1.4.1 Artificial Intelligence
			1.4.2 Neural Networks
			1.4.3 Data Mining
			1.4.4 Soft Computing
		1.5 Summary and Further Discussions
		References
	2 Numerical Vectors
		2.1 Introduction
		2.2 Operations on Numerical Vectors
			2.2.1 Definition
			2.2.2 Basic Operations
			2.2.3 Inner Product
			2.2.4 Linear Independence
		2.3 Operations on Matrices
			2.3.1 Definition
			2.3.2 Basic Operations
			2.3.3 Multiplication
			2.3.4 Inverse Matrix
		2.4 Vector and Matrix
			2.4.1 Determinant
			2.4.2 Eigen Value and Vector
			2.4.3 Singular Value Decomposition
			2.4.4 Principal Component Analysis
		2.5 Summary and Further Discussions
	3 Data Encoding
		3.1 Introduction
		3.2 Relational Data
			3.2.1 Basic Concepts
			3.2.2 Relational Database
			3.2.3 Encoding Process
			3.2.4 Encoding Issues
		3.3 Textual Data
			3.3.1 Text Indexing
			3.3.2 Text Encoding
			3.3.3 Dimension Reduction
			3.3.4 Encoding Issues
		3.4 Image Data
			3.4.1 Image File Formats
			3.4.2 Image Matrix
			3.4.3 Encoding Process
			3.4.4 Encoding Issues
		3.5 Summary and Further Discussions
		References
	4 Simple Machine Learning Algorithms
		4.1 Introduction
		4.2 Classification
			4.2.1 Binary Classification
			4.2.2 Multiple Classification
			4.2.3 Regression
			4.2.4 Problem Decomposition
		4.3 Simple Classifiers
			4.3.1 Threshold Rule
			4.3.2 Rectangle
			4.3.3 Hyperplane
			4.3.4 Matching Algorithm
		4.4 Linear Classifiers
			4.4.1 Linear Separability
			4.4.2 Hyperplane Equation
			4.4.3 Linear Classification
			4.4.4 Perceptron
		4.5 Summary and Further Discussions
		References
Part II Supervised Learning
	5 Instance Based Learning
		5.1 Introduction
		5.2 Primitive Instance Based Learning
			5.2.1 Look-Up Example
			5.2.2 Rule Based Approach
			5.2.3 Example Similarity
			5.2.4 One Nearest Neighbor
		5.3 Classification Process
			5.3.1 Notations
			5.3.2 Nearest Neighbors
			5.3.3 Voting
			5.3.4 Attribute Discriminations
		5.4 Variants
			5.4.1 Dynamic Nearest Neighbor
			5.4.2 Concentric Nearest Neighbor
			5.4.3 Hierarchical Nearest Neighbor
			5.4.4 Hub Examples
		5.5 Summary and Further Discussions
		References
	6 Probabilistic Learning
		6.1 Introduction
		6.2 Bayes Classifier
			6.2.1 Probabilities
			6.2.2 Bayes Rule
			6.2.3 Gaussian Distribution
			6.2.4 Classification
		6.3 Naive Bayes
			6.3.1 Classification
			6.3.2 Learning
			6.3.3 Variants
			6.3.4 Application to Text Classification
		6.4 Bayesian Learning
			6.4.1 Bayesian Networks
			6.4.2 Causal Relation
			6.4.3 Learning Process
			6.4.4 Comparisons
		6.5 Summary and Further Discussions
		References
	7 Decision Tree
		7.1 Introduction
		7.2 Classification Process
			7.2.1 Basic Structure
			7.2.2 Toy Examples
			7.2.3 Text Classification
			7.2.4 Rule Extraction
		7.3 Learning Process
			7.3.1 Preprocessing
			7.3.2 Root Node
			7.3.3 Interior Nodes
			7.3.4 Pruning
		7.4 Variants
			7.4.1 Regression Version
			7.4.2 Decision List
			7.4.3 Random Forest
			7.4.4 Decision Graph
		7.5 Summary and Further Discussions
		Reference
	8 Support Vector Machine
		8.1 Introduction
		8.2 Classification Process
			8.2.1 Linear Classifier
			8.2.2 Kernel Functions
			8.2.3 Lagrange Multipliers
			8.2.4 Generalization
		8.3 Learning Process
			8.3.1 Primal Problem
			8.3.2 Dual Problem
			8.3.3 SMO Algorithm
			8.3.4 Other Optimization Schemes
		8.4 Variants
			8.4.1 Fuzzy SVM
			8.4.2 Pairwise SVM
			8.4.3 LMS SVM
			8.4.4 Sparse SVM
		8.5 Summary and Further Discussions
		References
Part III Unsupervised Learning
	9 Simple Clustering Algorithms
		9.1 Introduction
		9.2 AHC Algorithm
			9.2.1 Cluster Similarity
			9.2.2 Initial Version
			9.2.3 Fuzzy Clustering
			9.2.4 Variants
		9.3 Divisive Clustering Algorithm
			9.3.1 Binary Clustering
			9.3.2 Evolutionary Binary Clustering
			9.3.3 Standard Version
			9.3.4 Variants
		9.4 Online Linear Clustering Algorithm
			9.4.1 Representative Selection Scheme
			9.4.2 Initial Version
			9.4.3 Fuzzy Clustering
			9.4.4 Variants
		9.5 Summary and Further Discussions
		References
	10 K Means Algorithm
		10.1 Introduction
		10.2 Supervised and Unsupervised Learning
			10.2.1 Learning Paradigm Transition
			10.2.2 Unsupervised KNN
			10.2.3 Semi-supervised KNN
			10.2.4 Dynamic Data Organization
		10.3 Clustering Process
			10.3.1 Initialization
			10.3.2 Hard Clustering
			10.3.3 Fuzzy Clustering
			10.3.4 Hierarchical Clustering
		10.4 Variants
			10.4.1 K Medoid Algorithm
			10.4.2 Dynamic K Means Algorithm
			10.4.3 Semi-supervised Version
			10.4.4 Constraint Clustering
		10.5 Summary and Further Discussions
		References
	11 EM Algorithm
		11.1 Introduction
		11.2 Cluster Distributions
			11.2.1 Uniform Distribution
			11.2.2 Gaussian Distribution
			11.2.3 Poisson Distribution
			11.2.4 Fuzzy Distributions
		11.3 Clustering Process
			11.3.1 Initialization
			11.3.2 E-Step
			11.3.3 M-Step
			11.3.4 Issues
		11.4 Semi-Supervised Learning: Text Classification
			11.4.1 Semi-Supervised Learning
			11.4.2 Initialization
			11.4.3 Likelihood Estimation
			11.4.4 Parameter Estimation
		11.5 Summary and Further Discussions
		References
	12 Advanced Clustering
		12.1 Introduction
		12.2 Cluster Index
			12.2.1 Computation Process
			12.2.2 Hard Clustering Evaluation
			12.2.3 Fuzzy Clustering Evaluation
			12.2.4 Hierarchical Clustering Evaluation
		12.3 Parameter Tuning
			12.3.1 Clustering Index to Unlabeled Items
			12.3.2 Simple Clustering Algorithms
			12.3.3 K Means Algorithm
			12.3.4 Evolutionary Clustering
		12.4 Clustering Governance
			12.4.1 Cluster Naming
			12.4.2 Cluster Maintenance
			12.4.3 Multiple Viewed Clustering
			12.4.4 Clustering Results Integration
		12.5 Summary and Further Discussions
		References
Part IV Advanced Topics
	13 Ensemble Learning
		13.1 Introduction
		13.2 Combination Schemes
			13.2.1 Voting
			13.2.2 Expert Gates
			13.2.3 Cascading
			13.2.4 Cellular Model
		13.3 Meta-learning
			13.3.1 Voting
			13.3.2 Expert Gates
			13.3.3 Cascading
			13.3.4 Cellular Model
		13.4 Partition
			13.4.1 Training Set Partition
			13.4.2 Attribute Set Partition
			13.4.3 Architecture Partition
			13.4.4 Parallel and Distributed Learning
		13.5 Summary and Further Discussions
		References
	14 Semi-supervised Learning
		14.1 Introduction
		14.2 Kohonen Networks
			14.2.1 Initial Version
			14.2.2 Learning Vector Quantization
			14.2.3 Semi-supervised Version
			14.2.4 Kohonen Networks vs. K Means Algorithm
		14.3 Combined Learning Algorithms
			14.3.1 Combination Paradigms
			14.3.2 Simple Learning Algorithms
			14.3.3 K Means Algorithm + KNN Algorithm
			14.3.4 EM Algorithm + Naive Bayes
		14.4 Advanced Supervised Learning
			14.4.1 Resampling
			14.4.2 Virtual Training Example
			14.4.3 Co-Learning
			14.4.4 Incremental Learning
		14.5 Summary and Further Discussions
		References
	15 Temporal Learning
		15.1 Introduction
		15.2 Discrete Markov Model
			15.2.1 State Diagram
			15.2.2 State Transition Probability
			15.2.3 State Path Probability
			15.2.4 Application to Time Series Prediction
		15.3 Hidden Markov Model
			15.3.1 Initial Parameters
			15.3.2 Observation Sequence Probability
			15.3.3 State Sequence Estimation
			15.3.4 HMM Learning
		15.4 Text Topic Analysis
			15.4.1 Task Specification
			15.4.2 Sampling
			15.4.3 Learning
			15.4.4 Topic Sequence
		15.5 Summary and Further Discussions
		References
	16 Reinforcement Learning
		16.1 Introduction
		16.2 Simple Reinforcement Learning
			16.2.1 Single Example
			16.2.2 Classification
			16.2.3 Regression
			16.2.4 Autonomous Moving
		16.3 Q Learning
			16.3.1 Q Table
			16.3.2 Finite State
			16.3.3 Infinite State
			16.3.4 Stochastic Reward
		16.4 Advanced Reinforcement Learning
			16.4.1 Ensemble Reinforcement Learning
			16.4.2 Reinforcement + Supervised
			16.4.3 Reinforcement + Unsupervised
			16.4.4 Environment Prediction
		16.5 Summary and Further Discussions
Index