User:Sooshie/Books/Machine Learning
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Introduction and Main Principles
[edit]- Machine learning
- Data analysis
- Occam's razor
- Curse of dimensionality
- No free lunch theorem
- Accuracy paradox
- Overfitting
- Regularization (machine learning)
- Inductive bias
- Data dredging
- Ugly duckling theorem
- Uncertain data
Background and Preliminaries
[edit]Knowledge discovery
[edit]In Databases
[edit]- Knowledge discovery
- Data mining
- Predictive analytics
- Predictive modelling
- Business intelligence
- Reactive business intelligence
- Business analytics
- Reactive business intelligence
- Pattern recognition
Reasoning
[edit]- Abductive reasoning
- Inductive reasoning
- First-order logic
- Inductive logic programming
- Reasoning system
- Case-based reasoning
- Textual case based reasoning
- Causality
- 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
- Tabu search
- Anytime algorithm
Statistics
[edit]Intro/Basics
[edit]- Exploratory data analysis
- Probability_distribution
- Variance
- Analysis_of_Variance
- Covariate
- Statistical inference
- Algorithmic inference
- Bayesian inference
- Base rate
- Bias (statistics)
- Gibbs sampling
- Cross-entropy method
- Latent variable
- Maximum likelihood
- Maximum a posteriori estimation
- Expectation–maximization algorithm
- Expectation propagation
- Kullback–Leibler divergence
- Generative model
Significance
[edit]- Likelihood_ratio_test
- Maximum_likelihood
- Statistical_significance
- Chi-squared_test
- G-test
- Pearson's_chi-squared_test
- Yates's_correction_for_continuity
- McNemar's_test
Machine Learning
[edit]Main Learning Paradigms
[edit]- Supervised learning
- Unsupervised learning
- Active learning (machine learning)
- Reinforcement learning
- Multi-task learning
- Transduction
- Explanation-based learning
- Offline learning
- Online learning model
- Online machine learning
- Hyperparameter optimization
Learning Theory
[edit]- Computational learning theory
- Version space
- Probably approximately correct learning
- Vapnik–Chervonenkis theory
- Shattering (machine learning)
- VC dimension
- Minimum description length
- Bondy's theorem
- Inferential theory of learning
- Rademacher complexity
- Teaching dimension
- Subclass reachability
- Sample exclusion dimension
- Unique negative dimension
- Uniform convergence (combinatorics)
- Witness set
Features Selection and Features Extraction
[edit]- 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
- Isomap
- Manifold alignment
- Diffusion map
- Elastic map
- Locality-sensitive hashing
- Spectral clustering
- Minimum redundancy feature selection
Association rules and Frequent Item Sets
[edit]- Association rule learning
- Apriori algorithm
- Contrast set learning
- Affinity analysis
- K-optimal pattern discovery
Regression analysis
[edit]- Outline of regression analysis
- Regression analysis
- Dependent and independent variables
- Linear model
- Linear regression
- Least squares
- Linear least squares (mathematics)
- Local regression
- Additive model
- Antecedent variable
- Autocorrelation
- 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's Cp
- Mean and predicted response
- Mixed model
- Moderation (statistics)
- Moving least squares
- Multicollinearity
- Multiple correlation
- Multivariate probit
- Multivariate adaptive regression splines
- Newey–West estimator
- Non-linear least squares
- Nonlinear regression
Logistic Regression
[edit]Classification
[edit]Intro
[edit]- Classification in machine learning
- Concept class
- Features (pattern recognition)
- Feature vector
- Feature space
- Concept learning
- Binary classification
- Decision boundary
- Multiclass classification
- Class membership probabilities
- Calibration (statistics)
- Concept drift
- Prior knowledge for pattern recognition
- Iris flower data set
Online Learning
[edit]Semi-supervised learning
[edit]Lazy learning and nearest neighbors
[edit]- Lazy learning
- Eager learning
- Instance-based learning
- Cluster assumption
- K-nearest neighbor algorithm
- IDistance
- Large margin nearest neighbor
Decision Trees
[edit]- 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
- CHAID
- Information Fuzzy Networks
- Grafting (decision trees)
- Incremental decision tree
- Alternating decision tree
- Logistic model tree
- Random forest
Linear Classifiers
[edit]- Linear classifier
- Margin (machine learning)
- Margin classifier
- Soft independent modelling of class analogies
- Statistical classification
- Statistical classification
- Probability matching
- Discriminative model
- 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
[edit]- Data classification (business intelligence)
- Training set
- Test set
- Synthetic data
- Cross-validation (statistics)
- Loss function
- Hinge loss
- 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
Clustering
[edit]Clustering Algorithms
[edit]- Cluster analysis
- K-means clustering
- K-means++
- K-medians clustering
- K-medoids
- DBSCAN
- Fuzzy clustering
- BIRCH (data clustering)
- Canopy clustering algorithm
- Cluster-weighted modeling
- Clustering high-dimensional data
- Cobweb (clustering)
- Complete-linkage clustering
- Constrained clustering
- Correlation clustering
- CURE data clustering algorithm
- Data stream clustering
- Dendrogram
- Determining the number of clusters in a data set
- FLAME clustering
- Hierarchical clustering
- Information bottleneck method
- Lloyd's algorithm
- Mean-shift
- Nearest-neighbor chain algorithm
- Neighbor joining
- OPTICS algorithm
- Pitman–Yor process
- Single-linkage clustering
- SUBCLU
- Thresholding (image processing)
- UPGMA
Support Vector Machines
[edit]- Kernel methods
- Support vector machine
- Structural risk minimization
- Empirical risk minimization
- Kernel trick
- Least squares support vector machine
- Relevance vector machine
- Sequential minimal optimization
- Structured SVM
Evaluation of Clustering Methods
[edit]- Rand index
- Dunn index
- Davies–Bouldin index
- Jaccard index
- MinHash
- K q-flats
- Rule Induction
- Decision rules
- Rule induction
- Classification rule
- CN2 algorithm
- Decision list
- First Order Inductive Learner
Ensemble Learning
[edit]- Ensemble learning
- Ensemble averaging
- Consensus clustering
- AdaBoost
- Boosting
- Bootstrap aggregating
- BrownBoost
- Cascading classifiers
- Co-training
- CoBoosting
- Gaussian process emulator
- Gradient boosting
- LogitBoost
- LPBoost
- Mixture model
- Product of Experts
- Random multinomial logit
- Random subspace method
- Weighted Majority Algorithm
- Randomized weighted majority algorithm
Graphical Models
[edit]Bayesian Learning Methods
[edit]- Naive Bayes classifier
- Averaged one-dependence estimators
- Bayesian network
- Bayesian additive regression kernels
- Variational message passing
Markov Models
[edit]- 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
- Predictive state representation
Reinforcement learning
[edit]- Reinforcement learning
- Markov decision process
- Bellman equation
- Q-learning
- Temporal difference learning
- SARSA
- Multi-armed bandit
- Apprenticeship learning
- Predictive learning
Advanced Learning Tasks
[edit]- 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
- Relational classification
- Data stream mining
- Alpha algorithm
- Syntactic pattern recognition
- Multispectral pattern recognition
- Algorithmic learning theory
- Deep learning
- Bongard problem
- Learning with errors
- Parity learning
- Inductive transfer
- Granular computing
- Conceptual clustering
- Formal concept analysis
- Biclustering
- Information visualization
- Co-occurrence networks
Bio-inspired Methods
[edit]Evolutionary Algorithms
[edit]- Evolvability (computer science)
- Evolutionary computation
- Evolutionary algorithm
- Genetic algorithm
- Chromosome (genetic algorithm)
- Crossover (genetic algorithm)
- Fitness function
- Evolutionary data mining
- Genetic programming
- Learnable Evolution Model
Neural Networks
[edit]- Neural network
- Artificial neural network
- Artificial neuron
- Types of artificial neural networks
- Perceptron
- Multilayer perceptron
- Activation function
- Self-organizing map
- Attractor network
- ADALINE
- Adaptive Neuro Fuzzy Inference System
- Adaptive resonance theory
- IPO underpricing algorithm
- ALOPEX
- Artificial Intelligence System
- Autoassociative memory
- Autoencoder
- Backpropagation
- Bcpnn
- Bidirectional associative memory
- Biological neural network
- Boltzmann machine
- Restricted Boltzmann machine
- 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
- Feed-forward
- Feedforward neural network
- 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
- HyperNEAT
- Infomax
- Instantaneously trained neural networks
- Interactive Activation and Competition
- Leabra
- Learning Vector Quantization
- Lernmatrix
- Linde–Buzo–Gray algorithm
- Liquid state machine
- Long short term memory
- Madaline
- Modular neural networks
- MoneyBee
- Neocognitron
- Nervous system network models
- NETtalk (artificial neural network)
- Neural backpropagation
- Neural coding
- Neural cryptography
- Neural decoding
- Neural gas
- Neural Information Processing Systems
- Neural modeling fields
- Neural oscillation
- Neurally controlled animat
- Neuroevolution of augmenting topologies
- Neuroplasticity
- Ni1000
- 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
- Rprop
- Semantic neural network
- Sigmoid function
- SNARC
- Softmax activation function
- Spiking neural network
- Stochastic neural network
- Synaptic plasticity
- Synaptic weight
- Tensor product network
- Time delay neural network
- U-Matrix
- Universal approximation theorem
- Winner-take-all
- Winnow (algorithm)
Data Mining
[edit]Text Mining
[edit]- Text mining
- Natural language processing
- Document classification
- Bag of words model
- N-gram
- 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
Structure Mining
[edit]- Structure mining
- Structured learning
- Structured prediction
- Sequence mining
- Sequence labeling
- Process mining
Anomaly Detection
[edit]Applications
[edit]- Problem domain
- Recommender system
- Collaborative filtering
- Profiling (information science)
- 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
- Computer vision
- Facial recognition system
- Outlier detection
- Anomaly detection
- Novelty detection
Software
[edit]- R (programming language)
- MapReduce
- Oracle Data Mining
- Pentaho
- Mallet (software project)
- Orange (software)
- Learning Based Java
- Scikit-learn
- Waffles (machine learning)
- Apache Mahout
- Data Applied
- Data Mining Extensions
- ELKI
- Feature Selection Toolbox
- Monte Carlo Machine Learning Library (MCMLL)
- Neural network software
- Software mining
