Category:Machine learning

A

• ► Applied machine learning‎ (14 P)
• ► Artificial intelligence conferences‎ (10 P)

B

• ► Bayesian networks‎ (10 P)

C

• ► Classification algorithms‎ (1 C, 73 P)
• ► Cluster analysis‎ (2 C, 15 P)
• ► Computational learning theory‎ (14 P)

D

• ► Data mining and machine learning software‎ (35 P)

D cont.

• ► Decision trees‎ (17 P)
• ► Dimension reduction‎ (16 P)

E

• ► Ensemble learning‎ (14 P)
• ► Evolutionary algorithms‎ (3 C, 41 P, 3 F)

I

• ► Inductive logic programming‎ (4 P)

K

• ► Kernel methods for machine learning‎ (1 C, 7 P)

L

• ► Latent variable models‎ (21 P)

L cont.

• ► Learning in computer vision‎ (6 P)
• ► Loss functions‎ (7 P)

M

• ► Machine learning algorithms‎ (49 P)
• ► Markov models‎ (3 C, 48 P)

N

• ► Neural networks‎ (1 C, 111 P, 1 F)

R

• ► Machine learning researchers‎ (35 P)

S

• ► Support vector machines‎ (5 P)

• Machine learning
• Stability (learning theory)

A

• Accuracy paradox
• Active learning (machine learning)
• Adjusted mutual information
• Algorithmic inference
• Apprenticeship learning

B

• Bag of words model
• Base rate
• Binary classification
• Bongard problem

C

• Category utility
• CBCL (MIT)
• User:ChrisSwetenham/PPCA
• CIML community portal
• Classification in machine learning
• Classifier chains
• Cluster analysis
• Computational learning theory
• Concept drift
• Concept learning
• Conditional random field
• Conference on Neural Information Processing Systems
• Confusion matrix
• Coupled pattern learner
• Cross-entropy method
• Cross-validation (statistics)
• Curse of dimensionality

D

• DARPA LAGR Program
• Data pre-processing
• Decision list
• Decision rules
• Decision tree learning
• Deep learning
• User:Dfindrik
• Dimension reduction
• Discriminative model
• Document classification

E

• Eager learning
• Early stopping

E cont.

• ECML PKDD
• Elastic matching
• Empirical risk minimization
• Evolvability (computer science)
• Expectation propagation
• Explanation-based learning

F

• Feature scaling
• Feature space
• Feature vector
• Formal concept analysis

G

• Generative model
• Granular computing
• Grid search

H

• Hinge loss

I

• IAPR
• Inductive bias
• Inductive transfer
• Inferential theory of learning
• Instance-based learning
• International Conference on Machine Learning
• Iris flower data set

J

• Journal of Machine Learning Research

K

• K q-flats
• Knowledge discovery
• Knowledge integration

L

• Lazy learning
• Learning automata
• Learning to rank
• Learning with errors

M

• Machine Learning (journal)
• Margin (machine learning)
• Matthews correlation coefficient
• Meta learning (computer science)
• Mixture model
• Motor babbling
• Mountain Car
• Multi-armed bandit
• Multi-task learning
• Multilinear principal-component analysis
• Multilinear subspace learning

M cont.

• Multiple-instance learning
• Multivariate adaptive regression splines

N

• Nearest neighbor search
• Neural modeling fields

O

• Offline learning
• Overfitting

P

• Parity learning
• Pattern recognition
• Predictive learning
• Predictive state representation
• Preference learning
• Prior knowledge for pattern recognition
• Proactive learning
• Probability matching
• Product of Experts

R

• Rademacher complexity
• Rand index
• Robot learning
• Rule induction

S

• Semantic analysis (machine learning)
• Semi-supervised learning
• Sequence labeling
• Smart variables
• Statistical classification
• Statistical relational learning
• Structural risk minimization
• Structured learning
• Subclass reachability
• Supervised learning

T

• Test set
• Training set
• Transduction (machine learning)

U

• Ugly duckling theorem
• Uncertain data
• Uniform convergence (combinatorics)
• Unsupervised learning

V

• Version space

W

• Ward's method