User:Liorrokach/Books/Machine Learning

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Machine Learning
The Complete Wikipedia Guide
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Machine Learning[edit]

Introduction and Main Principles
Machine learning
Data analysis
Occam's razor
Curse of dimensionality
No free lunch theorem
Accuracy paradox
Regularization (machine learning)
Inductive bias
Data dredging
Ugly duckling theorem

Background and Preliminaries
Knowledge discovery in Databases
Knowledge discovery
Data mining
Predictive analytics
Predictive modelling
Abductive reasoning
Inductive reasoning
First-order logic
Inductive logic programming
Reasoning system
Case-based reasoning
Textual case based reasoning
Search Methods
Nearest neighbor search
Stochastic gradient descent
Beam search
Best-first search
Breadth-first search
Hill climbing
Grid search
Brute-force search
Depth-first search
Anytime algorithm
Exploratory data analysis
Statistical inference
Algorithmic inference
Bayesian inference
Base rate
Bias (statistics)
Gibbs sampling
Cross-entropy method
Latent variable
Kullback–Leibler divergence
Main Learning Paradigms
Supervised learning
Unsupervised learning
Active learning (machine learning)
Reinforcement learning
Multi-task learning
Explanation-based learning
Offline learning
Online learning model
Online machine learning

Classification Tasks
Classification in machine learning
Concept class
Features (pattern recognition)
Feature vector
Feature space
Concept learning
Binary classification
Decision boundary
Multiclass classification
Iris flower data set
Class membership probabilities
Calibration (statistics)
Concept drift
Prior knowledge for pattern recognition

Online Learning
Margin Infused Relaxed Algorithm

Semi-supervised learning
Semi-supervised learning
[[PU learning]
One-class classification
Coupled pattern learner

Lazy learning and nearest neighbors
Lazy learning
Instance-based learning
Cluster assumption
K-nearest neighbor algorithm
Large margin nearest neighbor

Decision Trees
Decision tree learning
Decision stump
Pruning (decision trees)
Mutual information
Adjusted mutual information
Information gain ratio
Information gain in decision trees
ID3 algorithm
C4.5 algorithm
Information Fuzzy Networks
Grafting (decision trees)
Incremental decision tree
Alternating decision tree
Logistic model tree
Random forest

Linear Classifiers
Linear classifier
Margin (machine learning)
Margin classifier
Soft independent modelling of class analogies

Statistical classification
Statistical classification
Linear discriminant analysis
Multiclass LDA
Multiple discriminant analysis
Optimal discriminant analysis
Fisher kernel
Discriminant function analysis
Multilinear subspace learning
Quadratic classifier
Variable kernel density estimation
Category utility
Evaluation of Classification Models
Data classification (business intelligence)
Training set
Test set
Cross-validation (statistics)
Loss function
Generalization error
Type I and type II errors
Sensitivity and specificity
Precision and recall
F1 score
Confusion matrix
Matthews correlation coefficient
Receiver operating characteristic
Lift (data mining)
Stability in learning

Features Selection and Features Extraction
Data Pre-processing
Discretization of continuous features
Feature selection
Feature extraction
Dimension reduction
Principal component analysis
Multilinear principal-component analysis
Multifactor dimensionality reduction
Targeted projection pursuit
Multidimensional scaling
Nonlinear dimensionality reduction
Kernel principal component analysis
Kernel eigenvoice
Gramian matrix
Gaussian process
Kernel adaptive filter
Manifold alignment
Diffusion maps
Diffusion map
Elastic map
Locality-sensitive hashing
Spectral clustering
Minimum redundancy feature selection

Cluster analysis
K-means clustering
K-medians clustering
Fuzzy clustering
BIRCH (data clustering)
Canopy clustering algorithm
Cluster-weighted modeling
Conceptual clustering
Cobweb (clustering)
Complete-linkage clustering
Constrained clustering
CURE data clustering algorithm
Data stream clustering
Expectation–maximization algorithm
FLAME clustering
Hierarchical clustering
Information bottleneck method
Lloyd's algorithm
Nearest-neighbor chain algorithm
Neighbor joining
OPTICS algorithm
Pitman–Yor process
Single-linkage clustering
Thresholding (image processing)
Clustering high-dimensional data
Correlation clustering
Determining the number of clusters in a data set

Evaluation of Clustering Methods
Rand index
Dunn index
Davies–Bouldin index
Jaccard index

Rule Induction
Decision rules
Rule induction
Classification rule
CN2 algorithm
Decision list
First Order Inductive Learner
Association rules and Frequent Item Sets
Association rule learning
Apriori algorithm
Contrast set learning
Affinity analysis
K-optimal pattern discovery
Ensemble Learning
Ensemble learning
Ensemble averaging
Consensus clustering
Bootstrap aggregating
Cascading classifiers
Gaussian process emulator
Gradient boosting
Random multinomial logit
Random subspace method
Weighted Majority Algorithm
Randomized weighted majority algorithm

Graphical Models
Graphical model
Bayesian Learning Methods
Naive Bayes classifier
Averaged one-dependence estimators
Bayesian network
Bayesian additive regression kernels
Markov Models
Markov model
Maximum-entropy Markov model
Hidden Markov model
Baum–Welch algorithm
Forward–backward algorithm
Hierarchical hidden Markov model
Markov logic network
Markov chain Monte Carlo
Markov random field
Conditional random field

Learning Theory
Computational learning theory
Version space
Probably approximately correct learning
Vapnik–Chervonenkis theory
Shattering (machine learning)
VC dimension
Minimum description length
Witness set
Teaching dimension
Subclass reachability
Sample exclusion dimension
Unique negative dimension
Uniform convergence (combinatorics)
Bondy's theorem
Support Vector Machines
Kernel methods
Support vector machine
Structural risk minimization
Empirical risk minimization
Kernel trick
Structured SVM
Relevance vector machine
Least squares support vector machine
Sequential minimal optimization

Regression analysis
Outline of regression analysis
Regression analysis
Dependent and independent variables
Linear model
Linear regression
Least squares
Linear least squares (mathematics)
Tikhonov regularization (Ridge regression)
Local regression
Additive model
Antecedent variable
Backfitting algorithm
Bayesian linear regression
Bayesian multivariate linear regression
Binomial regression
Canonical analysis
Censored regression model
Coefficient of determination
Comparison of general and generalized linear models
Compressed sensing
Conditional change model
Controlling for a variable
Cross-sectional regression
Curve fitting
Deming regression
Design matrix
Difference in differences
Dummy variable (statistics)
Errors and residuals in statistics
Errors-in-variables models
Explained sum of squares
Explained variation
First-hitting-time model
Fixed effects model
Fraction of variance unexplained
Frisch–Waugh–Lovell theorem
General linear model
Generalized additive model
Generalized additive model for location, scale and shape
Generalized estimating equation
Generalized least squares
Generalized linear array model
Generalized linear mixed model
Generalized linear model
Growth curve
Guess value
Hat matrix
Heckman correction
Heteroscedasticity-consistent standard errors
Hosmer–Lemeshow test
Instrumental variable
Interaction (statistics)
Isotonic regression
Iteratively reweighted least squares
Kitchen sink regression
Lack-of-fit sum of squares
Leverage (statistics)
Limited dependent variable
Linear probability model
Mallows' Cp
Mean and predicted response
Mixed model
Moderation (statistics)
Moving least squares
Multiple correlation
Multivariate probit
Newey–West estimator
Non-linear least squares
Nonlinear regression
Logistic Regression
Multinomial logit
Logistic regression

Bio-inspired Methods
Bio-inspired computing
Ant colony optimization algorithms
Evolutionary Algorithms
Evolutionary computation
Evolutionary algorithm
Genetic algorithm
Chromosome (genetic algorithm)
Crossover (genetic algorithm)
Fitness function
Evolutionary data mining
Genetic programming
Learnable Evolution Model
Neural Networks
Neural network
Artificial neural network
Artificial neuron
Types of artificial neural networks
Multilayer perceptron
Activation function
Self-organizing map
Attractor network
Diffusion Networks
Adaptive Neuro Fuzzy Inference System
Adaptive resonance theory
IPO underpricing algorithm
Artificial Intelligence System
Autoassociative memory
Bidirectional associative memory
Biological neural network
Boltzmann machine
Cascade correlation algorithm
Cellular neural network
Cerebellar Model Articulation Controller
Committee machine
Competitive learning
Compositional pattern-producing network
Computational cybernetics
Computational neurogenetic modeling
Confabulation (neural networks)
Cortical column
Counterpropagation network
Cover's theorem
Cultured neuronal network
Dehaene-Changeux Model
Delta rule
Early stopping
Echo state network
The Emotion Machine
Evolutionary Acquisition of Neural Topologies
Extension neural network
Feedforward neural network
Fuzzy cellular neural networks
Generalized Hebbian Algorithm
Generative topographic map
Group method of data handling
Growing self-organizing map
Memory-prediction framework
Helmholtz machine
Hierarchical temporal memory
Hopfield network
Hybrid neural network
Instantaneously trained neural networks
Interactive Activation and Competition
Learning Vector Quantization
Linde–Buzo–Gray algorithm
Liquid state machine
Long short term memory
Modular neural networks
Nervous system network models
NETtalk (artificial neural network)
Neural backpropagation
Neural coding
Neural cryptography
Neural decoding
Neural gas
Neural Information Processing Systems
Neural oscillation
Neurally controlled animat
Neuroevolution of augmenting topologies
Nonspiking neurons
Nonsynaptic plasticity
Oja's rule
Optical neural network
Phase-of-firing code
Promoter based genetic algorithm
Pulse-coupled networks
Quantum neural network
Radial basis function
Radial basis function network
Random neural network
Recurrent neural network
Reentry (neural circuitry)
Reservoir computing
Semantic neural network
Sigmoid function
Softmax activation function
Spiking neural network
Stochastic neural network
Synaptic plasticity
Synaptic weight
Tensor product network
Time delay neural network
Universal approximation theorem
Winnow (algorithm)

Reinforcement learning
Reinforcement learning
Markov decision process
Bellman equation
Temporal difference learning
Multi-armed bandit
Apprenticeship learning
Text Mining
Text mining
Natural language processing
Document classification
Bag of words model
Part-of-speech tagging
Sentiment analysis
Information extraction
Topic model
Concept mining
Semantic analysis (machine learning)
Automatic summarization
Automatic distillation of structure
String kernel
Biomedical text mining
Never-Ending Language Learning

Stucture Mining
Structure mining
Structured learning
Structured prediction
Sequence mining
Sequence labeling
Process mining

Advanced Learning Tasks
Multi-label classification
Classifier chains
Web mining
Anomaly detection
Anomaly Detection at Multiple Scales
Local outlier factor
Novelty detection
GSP Algorithm
Optimal matching
Record linkage
Meta learning (computer science)
Learning automata
Learning to rank
Multiple-instance learning
Statistical relational learning
Data stream mining
Alpha algorithm
Syntactic pattern recognition
Multispectral pattern recognition
Algorithmic learning theory
Deep learning
Bongard problem

Recommender system
Collaborative filtering
Profiling practices
Speech recognition
Stock forecast
Activity recognition
Data Analysis Techniques for Fraud Detection
Molecule mining
Predictive behavioral targeting
Proactive Discovery of Insider Threats Using Graph Analysis and Learning
Robot learning
R (programming language)
Oracle Data Mining
Mallet (software project)
Orange (software)
Learning Based Java
Waffles (machine learning)
Apache Mahout
Data Applied
Data Mining Extensions
Monte Carlo Machine Learning Library (MCMLL)
Software mining
Feature Selection Toolbox
Neural network software


Information visualization
Co-occurrence networks