User:Jqveenstra/Books/ml

From Wikipedia, the free encyclopedia


Committee machine
Machine learning
Accuracy paradox
Action model learning
Active learning (machine learning)
Adaptive projected subgradient method
Adversarial machine learning
AIXI
Algorithmic inference
Apprenticeship learning
Bag-of-words model
Ball tree
Base rate
Bayesian interpretation of regularization
Bayesian optimization
Bias–variance tradeoff
Binary classification
Bongard problem
Bradley–Terry model
Catastrophic interference
Category utility
CBCL (MIT)
CIML community portal
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
Data pre-processing
Decision list
Deep learning
Deeplearning4j
Developmental robotics
Dimensionality reduction
Discriminative model
Document classification
Domain adaptation
Eager learning
Early stopping
Elastic matching
Empirical risk minimization
Ensembles of classifiers
Evaluation of binary classifiers
Evolvability (computer science)
Expectation propagation
Explanation-based learning
Feature (machine learning)
Feature engineering
Feature hashing
Feature learning
Feature scaling
Feature vector
Formal concept analysis
Generative model
Google DeepMind
Grammar induction
Granular computing
Hyperparameter optimization
Inductive bias
Inductive functional programming
Inductive probability
Inductive programming
Inductive transfer
Inferential theory of learning
Instance-based learning
Instantaneously trained neural networks
Journal of Machine Learning Research
Kernel density estimation
Kernel embedding of distributions
Kernel random forest
Knowledge integration
Knowledge Vault
Large margin nearest neighbor
Lazy learning
Learning automata
Learning to rank
Learning with errors
Leave-one-out error
Linear predictor function
Linear separability
Local case-control sampling
M-Theory (learning framework)
Logic learning machine
Machine Learning (journal)
Matthews correlation coefficient
Meta learning (computer science)
Mixture model
Mountain Car
Multi-armed bandit
Multi-task learning
Multilinear principal component analysis
Multilinear subspace learning
Multiple-instance learning
Multivariate adaptive regression splines
Native-language identification
Nearest neighbor search
Neural modeling fields
Occam learning
Offline learning
OpenNN
Overfitting
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
Quantum machine learning
Query level feature
Rademacher complexity
Random indexing
Random projection
Representer theorem
Robot learning
Rule induction
Sample complexity
Semantic analysis (machine learning)
Semi-supervised learning
Sequence labeling
Similarity learning
Solomonoff's theory of inductive inference
Stability (learning theory)
Statistical classification
Statistical learning theory
Statistical relational learning
Structural risk minimization
Subclass reachability
Supervised learning
Test set
Transduction (machine learning)
Ugly duckling theorem
Uncertain data
Uniform convergence (combinatorics)
Universal portfolio algorithm
Unsupervised learning
User behavior analytics
Vanishing gradient problem
Version space learning
Zeroth (software)