User:Mathurin.ache: Difference between revisions
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|title=Machine Learning |
|title=Machine Learning: |
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|subtitle= |
|subtitle=The Complete Wikipedia Guide |
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|cover-image=Kernel_Machine.png |
|cover-image=Kernel_Machine.png |
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;Decision Trees |
;Decision Trees |
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:[[Decision tree learning]] |
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:[[Decision stump]] |
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:[[Pruning (decision trees)]] |
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:[[Mutual information]] |
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:[[Adjusted mutual information]] |
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:[[Information gain ratio]] |
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:[[Information gain in decision trees]] |
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:[[ID3 algorithm]] |
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:[[C4.5 algorithm]] |
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:[[CHAID]] |
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:[[Information Fuzzy Networks]] |
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:[[Grafting (decision trees)]] |
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:[[Incremental decision tree]] |
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:[[Alternating decision tree]] |
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:[[Logistic model tree]] |
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:[[Random forest]] |
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;Linear Classifiers |
;Linear Classifiers |
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:[[Linear classifier]] |
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:[[Margin (machine learning)]] |
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:[[Margin classifier]] |
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:[[Soft independent modelling of class analogies]] |
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;Statistical classification |
;Statistical classification |
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:[[Statistical classification]] |
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:[[Probability matching]] |
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:[[Discriminative model]] |
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:[[Linear discriminant analysis]] |
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:[[Multiclass LDA]] |
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:[[Multiple discriminant analysis]] |
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:[[Optimal discriminant analysis]] |
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:[[Fisher kernel]] |
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:[[Discriminant function analysis]] |
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:[[Multilinear subspace learning]] |
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:[[Quadratic classifier]] |
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:[[Variable kernel density estimation]] |
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:[[Category utility]] |
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;Evaluation of Classification Models |
;Evaluation of Classification Models |
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:[[Data classification (business intelligence)]] |
:[[Data classification (business intelligence)]] |
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:[[Conditional random field]] |
:[[Conditional random field]] |
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:[[Predictive state representation]] |
:[[Predictive state representation]] |
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;Learning Theory |
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:[[Computational learning theory]] |
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:[[Version space]] |
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:[[Probably approximately correct learning]] |
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:[[Vapnik–Chervonenkis theory]] |
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:[[Shattering (machine learning)]] |
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:[[VC dimension]] |
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:[[Minimum description length]] |
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:[[Bondy's theorem]] |
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:[[Inferential theory of learning]] |
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:[[Rademacher complexity]] |
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:[[Teaching dimension]] |
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:[[Subclass reachability]] |
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:[[Sample exclusion dimension]] |
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:[[Unique negative dimension]] |
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:[[Uniform convergence (combinatorics)]] |
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:[[Witness set]] |
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;Support Vector Machines |
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:[[Kernel methods]] |
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:[[Support vector machine]] |
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:[[Structural risk minimization]] |
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:[[Empirical risk minimization]] |
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:[[Kernel trick]] |
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:[[Least squares support vector machine]] |
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:[[Relevance vector machine]] |
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:[[Sequential minimal optimization]] |
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:[[Structured SVM]] |
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[[Category:Wikipedia books on computer science]] |
[[Category:Wikipedia books on computer science]] |
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Machine Learning
- Introduction and Main Principles
- 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
- Knowledge discovery in Databases
- Knowledge discovery
- Data mining
- Predictive analytics
- Predictive modelling
- Business intelligence
- Reactive business intelligence
- Business analytics
- Reactive business intelligence
- Pattern recognition
- Reasoning
- 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
- Exploratory data analysis
- 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
- Main Learning Paradigms
- 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
- Classification Tasks
- 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 (Classic data sets)
- Online Learning
- Margin Infused Relaxed Algorithm
- Semi-supervised learning
- Semi-supervised learning
- One-class classification
- Coupled pattern learner
- Lazy learning and nearest neighbors
- Lazy learning
- Eager learning
- Instance-based learning
- Cluster assumption
- K-nearest neighbor algorithm
- IDistance
- 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
- CHAID
- 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
- 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
- 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
- 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
- Isomap
- Manifold alignment
- Diffusion map
- Elastic map
- Locality-sensitive hashing
- Spectral clustering
- Minimum redundancy feature selection
- Clustering
- 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
- Nearest-neighbor chain algorithm
- Neighbor joining
- OPTICS algorithm
- Pitman–Yor process
- Single-linkage clustering
- SUBCLU
- Thresholding (image processing)
- UPGMA
- Evaluation of Clustering Methods
- 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
- 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
- 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
- Graphical model
- State transition network
- Bayesian Learning Methods
- Naive Bayes classifier
- Averaged one-dependence estimators
- Bayesian network
- Bayesian additive regression kernels
- Variational message passing
- 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
- Predictive state representation
- Learning Theory
- 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
- Support Vector Machines
- 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