Category:Machine learning

A

• ► Applied machine learning‎ (34 P)
• ► Artificial neural networks‎ (2 C, 125 P)

B

• ► Bayesian networks‎ (12 P)

C

• ► Classification algorithms‎ (3 C, 81 P)
• ► Cluster analysis‎ (2 C, 19 P)
• ► Computational learning theory‎ (21 P)
• ► Artificial intelligence conferences‎ (14 P)

D

• ► Data mining and machine learning software‎ (1 C, 83 P)
• ► Datasets in machine learning‎ (1 C, 5 P)
• ► Deep learning‎ (17 P)
• ► Dimension reduction‎ (1 C, 39 P)

E

• ► Ensemble learning‎ (14 P)
• ► Evolutionary algorithms‎ (4 C, 40 P, 2 F)

G

• ► Genetic programming‎ (12 P)

I

• ► Inductive logic programming‎ (5 P)

K

• ► Kernel methods for machine learning‎ (1 C, 15 P)

L

• ► Latent variable models‎ (2 C, 24 P)
• ► Learning in computer vision‎ (4 P)
• ► Log-linear models‎ (2 P)
• ► Loss functions‎ (8 P)

M

• ► Machine learning algorithms‎ (1 C, 55 P)
• ► Machine learning portal‎ (2 P)
• ► Machine learning task‎ (5 P)
• ► Markov models‎ (2 C, 51 P)

R

• ► Machine learning researchers‎ (72 P)

S

• ► Statistical natural language processing‎ (1 C, 35 P)
• ► Structured prediction‎ (1 C, 3 P)
• ► Support vector machines‎ (8 P)

• AI Bridging Cloud Infrastructure
• Machine learning
• Machine Teaching

A

• Accuracy paradox
• Action model learning
• Active learning (machine learning)
• Adversarial machine learning
• AIVA
• AIXI
• Algorithm Selection
• Algorithmic inference
• AlphaGo
• Apprenticeship learning

B

• Bag-of-words model
• Ball tree
• Base rate
• Bayesian interpretation of kernel regularization
• Bayesian optimization
• Bayesian structural time series
• Bias–variance tradeoff
• Binary classification
• Bing Predicts
• Bongard problem
• Bradley–Terry model

C

• Catastrophic interference
• Category utility
• CBCL (MIT)
• CIML community portal
• Cleverbot
• Cognitive robotics
• Committee machine
• Draft:Complexité de Rademacher
• Computational learning theory
• Concept drift
• Concept learning
• Conditional random field
• Confusion matrix
• Constrained conditional model
• Coupled pattern learner
• Cross-entropy method
• Cross-validation (statistics)
• Curse of dimensionality

D

• Darkforest
• Data pre-processing
• Dataiku
• Decision list
• Deep feature synthesis
• Deeplearning4j
• DeepMind
• Developmental robotics
• Dimensionality reduction
• Discriminative model
• Document classification
• Domain adaptation
• Dropout (neural networks)

E

• Eager learning
• Early stopping
• Elastic matching
• Empirical risk minimization
• Ensembles of classifiers
• Error Tolerance (PAC learning)
• Evaluation of binary classifiers
• Evolutionary programming
• Evolvability (computer science)
• Expectation propagation
• Explanation-based learning

F

• Feature (machine learning)
• Feature engineering
• Feature hashing
• Feature learning
• Feature scaling
• Feature vector
• Formal concept analysis

G

• Generative model
• Genetic algorithm
• Glossary of artificial intelligence
• Grammar induction
• Granular computing

H

• Holdout method
• Hyperparameter optimization

I

• Inauthentic text
• Inductive bias
• Inductive functional programming
• Inductive probability
• Inductive programming
• Inductive transfer
• Inferential theory of learning
• Instance-based learning
• Instantaneously trained neural networks
• Isotropic position

J

• Journal of Machine Learning Research

K

• Kernel density estimation
• Kernel embedding of distributions
• Kernel random forest
• Knowledge integration
• Knowledge Vault

L

• Large margin nearest neighbor
• Lazy learning
• Learnable function class
• Learning automata
• Learning to rank
• Learning with errors
• Leave-one-out error
• Linear predictor function
• Linear separability
• List of datasets for machine learning research
• Local case-control sampling

M

• M-Theory (learning framework)
• Logic learning machine
• Machine Learning (journal)
• Manifold regularization
• The Master Algorithm
• Matrix regularization
• Matthews correlation coefficient
• Meta learning (computer science)
• Mixture model
• Mountain Car
• Movidius (company)
• Multi-armed bandit
• Multi-task learning
• Multilinear principal component analysis
• Multilinear subspace learning
• Multiple instance learning
• Multiple-instance learning
• Multiplicative Weight Update Method
• Multivariate adaptive regression splines
• MysteryVibe

N

• Native-language identification
• Nearest neighbor search
• Neural modeling fields

O

• Occam learning
• Offline learning
• OpenNN
• Orange (software)
• Overfitting

P

• Parity learning
• Pattern language (formal languages)
• Pattern recognition
• Predictive learning
• Predictive state representation
• Preference learning
• Prior knowledge for pattern recognition
• Proactive learning
• Probability matching
• Product of experts
• Proximal gradient methods for learning

Q

• Quantum machine learning
• Query level feature

R

• Rademacher complexity
• Random indexing
• Random projection
• Relational data mining
• Representer theorem
• Robot learning
• Rule induction

S

• Sample complexity
• Savi Technology
• Semantic analysis (machine learning)
• Semantic folding
• Semi-supervised learning
• Sequence labeling
• Similarity learning
• Skymind
• Solomonoff's theory of inductive inference
• Sparse dictionary learning
• Spike-and-slab variable selection
• Stability (learning theory)
• Statistical classification
• Statistical learning theory
• Statistical relational learning
• Stochastic block model
• Structural risk minimization
• Structured sparsity regularization
• Subclass reachability
• Supervised learning

T

• Test set
• Timeline of machine learning
• Transduction (machine learning)
• Trax Image Recognition

U

• Ugly duckling theorem
• Uncertain data
• Uniform convergence (combinatorics)
• Universal portfolio algorithm
• Unsupervised learning
• User behavior analytics

V

• Validation set
• Vanishing gradient problem
• Version space learning

W

• Word2vec

Z

• Zeroth (software)