User:Zarzuelazen/Books/Reality Theory: Machine Learning

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Reality Theory: Machine Learning[edit]

3D pose estimation
Acoustic model
Activation function
Active learning (machine learning)
Adaptive resonance theory
Adversarial machine learning
Affine shape adaptation
AIXI
Algorithm selection
Ancestral graph
Anomaly detection
Artificial neural network
Artificial neuron
Association rule learning
Assortativity
Autoencoder
Automated machine learning
Automatic summarization
Backpropagation
Bag-of-words model in computer vision
Barabási–Albert model
Bayesian network
Belief propagation
Betweenness centrality
Bias–variance tradeoff
Bidirectional recurrent neural networks
Binary classification
Blob detection
Boltzmann machine
Boosting (machine learning)
Bootstrap aggregating
C4.5 algorithm
Canonical correlation
Capsule neural network
Catastrophic interference
Centrality
Cerebellar model articulation controller
CheiRank
Cluster analysis
Collaborative filtering
Community structure
Competitive learning
Complex network
Computational learning theory
Computer vision
Conceptual clustering
Conditional random field
Confusion matrix
Constellation model
Constrained conditional model
Content discovery platform
Content-based image retrieval
Convolutional neural network
Corner detection
Correspondence analysis
Cross-validation (statistics)
Curse of dimensionality
Data mining
Data pre-processing
Davies–Bouldin index
DBSCAN
Decision tree
Decision tree learning
Deep belief network
Deep learning
Degree distribution
Delta rule
Differentiable neural computer
Diffusion map
Dimensionality reduction
Discriminant function analysis
Discriminative model
Document classification
Document retrieval
Dunn index
Dynamic Bayesian network
Early stopping
Echo state network
Edge detection
Efficiency (network science)
Elastic map
Email filtering
Empirical risk minimization
Ensemble averaging (machine learning)
Ensemble learning
Erdős–Rényi model
Evolving networks
Example-based machine translation
Expectation–maximization algorithm
Exponential random graph models
Face detection
Facial recognition system
Factor analysis
Factor graph
Feature (computer vision)
Feature (machine learning)
Feature detection (computer vision)
Feature engineering
Feature extraction
Feature learning
Feature selection
Feature vector
Feedforward neural network
Fixed effects model
Forward algorithm
Forward–backward algorithm
Fusion adaptive resonance theory
Fuzzy clustering
General linear model
Generalization error
Generalized linear model
Generative adversarial networks
Generative model
Gesture recognition
Google Search
Gradient boosting
Graph drawing
Graphical model
Group method of data handling
Gödel machine
Handwriting recognition
Hidden Markov model
Hierarchical clustering
Hierarchical clustering of networks
Hierarchical Deep Learning
Hierarchical hidden Markov model
Hierarchical network model
Hierarchical temporal memory
HITS algorithm
Hopfield network
Hough transform
Hyperbolic geometric graph
Hyperparameter (machine learning)
Hyperparameter optimization
ID3 algorithm
Image segmentation
Incremental learning
Inductive bias
Information extraction
Information filtering system
Information retrieval
Information visualization
Instance selection
Instantaneously trained neural networks
Interest point detection
Junction tree algorithm
K-means clustering
K-nearest neighbors algorithm
Kernel density estimation
Kernel method
Kernel perceptron
Kernel principal component analysis
Lancichinetti–Fortunato–Radicchi benchmark
Language model
Lasso (statistics)
Latent class model
Latent variable model
Learning classifier system
Learning to rank
Learning vector quantization
Least squares
Least-angle regression
Linear classifier
Linear discriminant analysis
Linear regression
Linear separability
Link analysis
Liquid state machine
Local tangent space alignment
Logistic regression
Long short-term memory
Loss function
Machine learning
Machine translation
Manifold alignment
Manifold regularization
Markov chain
Markov chain Monte Carlo
Markov model
Markov random field
Maximum-entropy Markov model
Mean shift
Meta learning (computer science)
Method of moments (statistics)
Mixed logit
Mixture model
Modular neural network
Modularity (networks)
Motion estimation
Multi-label classification
Multi-task learning
Multiclass classification
Multidimensional scaling
Multilayer perceptron
Multilevel model
Multilinear principal component analysis
Multilinear subspace learning
Multinomial logistic regression
Multinomial probit
Naive Bayes classifier
Naive Bayes spam filtering
Network controllability
Network science
Network theory
Neural Turing machine
Non-linear least squares
Non-negative matrix factorization
Nonlinear dimensionality reduction
Nonlinear regression
Nonparametric regression
Object detection
Occam learning
One-shot learning
Online machine learning
Optical character recognition
Optical flow
OPTICS algorithm
Ordered logit
Ordered probit
Ordinal regression
Ordinary least squares
Outline of object recognition
Overfitting
PageRank
Part-based models
Pattern recognition
Perceptron
Platt scaling
Point set registration
Poisson regression
Polynomial regression
Pose (computer vision)
Precision and recall
Predictive analytics
Predictive modelling
Principal component analysis
Probabilistic neural network
Probably approximately correct learning
Probit model
Pruning (decision trees)
Question answering
Radial basis function
Radial basis function network
Random effects model
Random forest
Random subspace method
Ranking (information retrieval)
Receiver operating characteristic
Reciprocity (network science)
Recommender system
Rectifier (neural networks)
Recurrent neural network
Regression analysis
Regularization (mathematics)
Relevance vector machine
Reservoir computing
Residual neural network
Restricted Boltzmann machine
Ridge detection
Root-mean-square deviation
Rule-based machine learning
Scale space
Scale space implementation
Scale-free network
Scale-invariant feature transform
Scale-space axioms
Scale-space segmentation
Self-organizing map
Semi-supervised learning
Semidefinite embedding
Sequence labeling
Sequential pattern mining
Similarity learning
Simple linear regression
Small-world network
Softmax function
Solomonoff's theory of inductive inference
Sparse dictionary learning
Sparse distributed memory
Spectral clustering
Speech recognition
Speech synthesis
Spiking neural network
Stability (learning theory)
Statistical classification
Statistical learning theory
Statistical machine translation
Stochastic block model
Structural risk minimization
Structure mining
Structure tensor
Structured prediction
Structured support vector machine
Supervised learning
Support vector machine
TensorFlow
Text mining
Thresholding (image processing)
Tikhonov regularization
Time delay neural network
Training, test, and validation sets
Transfer learning
TrustRank
Types of artificial neural networks
Universal approximation theorem
Unsupervised learning
Vanishing gradient problem
Vapnik–Chervonenkis theory
Variable-order Markov model
Variational Bayesian methods
VC dimension
Vector quantization
Video tracking
Visual descriptor
Visual odometry
Watts–Strogatz model
Web search engine