User:Zarzuelazen/Books/Reality Theory: Neural Nets & Pattern Recognition

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Reality Theory: Neural Nets & Pattern Recognition[edit]

3D pose estimation
Acoustic model
Activation function
Active contour model
Active learning (machine learning)
Activity recognition
Adaptive resonance theory
Additive model
Adversarial machine learning
Affine shape adaptation
AKS primality test
Algorithm selection
Ancestral graph
Anomaly detection
Arnoldi iteration
Artificial neural network
Artificial neuron
Association rule learning
Assortativity
Attention (machine learning)
Autoassociative memory
Autoencoder
Automated machine learning
Automatic image annotation
Automatic summarization
Average path length
Backfitting algorithm
Backpropagation
Bag-of-words model in computer vision
Baillie–PSW primality test
Band matrix
Barabási–Albert model
Batch normalization
Baum–Welch algorithm
Bayesian hierarchical modeling
Bayesian linear regression
Bayesian multivariate linear regression
Bayesian network
Belief propagation
Betweenness centrality
Bias–variance tradeoff
Biclustering
Bicubic interpolation
Bidirectional associative memory
Bidirectional recurrent neural networks
Bilinear interpolation
Binary classification
Binary regression
Binomial regression
Blob detection
Boltzmann machine
Boolean network
Boosting (machine learning)
Bootstrap aggregating
Bradley–Terry model
Buchberger's algorithm
C4.5 algorithm
Caffe (software)
Camera matrix
Canonical correlation
Capsule neural network
Catastrophic interference
Centrality
Cerebellar model articulation controller
Cholesky decomposition
Chunking (division)
Closeness centrality
Cluster analysis
Cluster hypothesis
Cluster labeling
Clustering coefficient
Clustering high-dimensional data
Collective classification
Committee machine
Community search
Community structure
Competitive learning
Complete-linkage clustering
Complex network
Computational learning theory
Computational statistics
Computer audition
Computer stereo vision
Computer vision
Computing the permanent
Conceptual clustering
Conditional random field
Confirmatory factor analysis
Confusion matrix
Conjugate gradient method
Connected-component labeling
Consensus clustering
Constellation model
Cosine similarity
Constrained conditional model
Continuous-time Markov chain
Convergent matrix
Convolutional neural network
Correlation clustering
Corner detection
Correspondence analysis
Correspondence problem
Co-training
Cross-validation (statistics)
Curse of dimensionality
Curve fitting
Data mining
Data pre-processing
Davies–Bouldin index
DBSCAN
Decision boundary
Decision tree
Decision tree learning
Deep belief network
Deep learning
Degree distribution
Dehaene–Changeux model
Delta rule
Deming regression
Dependency network
Determining the number of clusters in a data set
Differentiable neural computer
Diffusion map
Dilution (neural networks)
Dimensionality reduction
Discrete-time Markov chain
Discriminant function analysis
Discriminative model
Distance matrix
Distribution learning theory
Divergence-from-randomness model
Divided differences
Domain adaptation
Dunn index
Dynamic Bayesian network
Dynamic time warping
Eager learning
Early stopping
Echo state network
Edge detection
Efficiency (network science)
Eigenvalue algorithm
Eigenvector centrality
Elastic map
Empirical risk minimization
Energy based model
Ensemble averaging (machine learning)
Ensemble learning
Erdős–Rényi model
Error tolerance (PAC learning)
Essential matrix
Euclidean algorithm
Evaluation measures (information retrieval)
Evaluation of binary classifiers
Evidence lower bound
Evolving networks
Expectation–maximization algorithm
Exploratory factor analysis
Exponential random graph models
Extrapolation
F1 score
Face detection
Facial recognition system
Factor analysis
Feature (computer vision)
Feature (machine learning)
Feature detection (computer vision)
Feature engineering
Feature extraction
Feature hashing
Feature learning
Feature scaling
Feature selection
Feature vector
Federated learning
Feedforward neural network
Fermat's factorization method
Fermat primality test
Fixed effects model
Foreground detection
Forward algorithm
Forward–backward algorithm
Function approximation
Fundamental matrix (computer vision)
Fusion adaptive resonance theory
Fuzzy clustering
Gated recurrent unit
Gaussian elimination
Gauss–Seidel method
General linear model
General number field sieve
Generalized Hebbian algorithm
Generalised Hough transform
Generalization error
Generalized additive model
Generalized least squares
Generalized linear model
Generative adversarial networks
Generative model
Generative topographic map
Gesture recognition
Gibbs sampling
Givens rotation
Gradient boosting
Gram–Schmidt process
Graph cuts in computer vision
Graph edit distance
Graph isomorphism problem
Graph matching
Graphical model
Grid method multiplication
Gröbner basis
Group method of data handling
Growth function
Handwriting recognition
Harris chain
Hermite interpolation
Hidden Markov model
Hierarchical classification
Hierarchical clustering
Hierarchical clustering of networks
Hierarchical Deep Learning
Hierarchical hidden Markov model
Hierarchical network model
Hierarchical temporal memory
Hinge loss
Histogram of oriented gradients
Hopfield network
Hough transform
Householder operator
Householder transformation
Hyperbolic geometric graph
Hyperparameter (machine learning)
Hyperparameter optimization
ID3 algorithm
Image segmentation
ImageNet
Importance sampling
Incremental learning
Inductive bias
Influence diagram
Information gain in decision trees
Instance-based learning
Instance selection
Instantaneously trained neural networks
Integer factorization
Interest point detection
Interpolation
Interval predictor model
Inverse iteration
Inverse transform sampling
Isotonic regression
Jaccard index
Jacobi eigenvalue algorithm
Jacobi method
Johnson–Lindenstrauss lemma
Junction tree algorithm
K-means clustering
K-medians clustering
k-medoids
K-nearest neighbors algorithm
Karatsuba algorithm
Katz centrality
Keras
Kernel density estimation
Kernel embedding of distributions
Kernel Fisher discriminant analysis
Kernel method
Kernel perceptron
Kernel principal component analysis
Kernel regression
Knowledge distillation
Kriging
Krylov subspace
Labeled data
Lancichinetti–Fortunato–Radicchi benchmark
Lanczos algorithm
Language model
Lasso (statistics)
Latent class model
Latent growth modeling
Latent variable model
Lattice multiplication
Lazy learning
Learning curve (machine learning)
Learning rate
Learning to rank
Learning vector quantization
Least squares
Least-angle regression
Linear classifier
Linear discriminant analysis
Linear interpolation
Linear regression
Linear separability
Link analysis
Link prediction
Liquid state machine
List of datasets for machine learning research
Local binary patterns
Local regression
Local tangent space alignment
Locality-sensitive hashing
Logistic regression
Logit
Long division
Long short-term memory
Loss functions for classification
Low-rank approximation
LU decomposition
Machine learning
Machine perception
Machine vision
Manifold alignment
Manifold regularization
Margin (machine learning)
Margin classifier
Markov blanket
Markov chain
Markov chain Monte Carlo
Markov chains on a measurable state space
Markov model
Markov random field
Matrix multiplication algorithm
Matrix splitting
Matthews correlation coefficient
Maximum-entropy Markov model
Mean field particle methods
Mean shift
Medoid
Meta learning (computer science)
Method of moments (statistics)
Metropolis–Hastings algorithm
Miller–Rabin primality test
Mixed logit
Mixed model
Mixing patterns
Mixture model
MNIST database
Modular neural network
Modularity (networks)
Monte Carlo method
Motion estimation
Moving object detection
Multi-label classification
Multi-task learning
Multiclass classification
Multidimensional scaling
Multilayer perceptron
Multilevel model
Multilinear principal component analysis
Multilinear subspace learning
Multimodal learning
Multinomial logistic regression
Multinomial probit
Multiple instance learning
Multivariate adaptive regression spline
Multivariate interpolation
Multivariate probit model
Multivariate kernel density estimation
Naive Bayes classifier
Neighborhood operation
Network controllability
Network motif
Network science
Network theory
Neural architecture search
Neural network Gaussian process
Neural Turing machine
Newton polynomial
Non-linear least squares
Non-negative matrix factorization
Nonlinear dimensionality reduction
Nonlinear regression
Nonparametric regression
Numerical linear algebra
NumPy
Object Co-segmentation
Object detection
Occam learning
Oja's rule
One-shot learning
Online machine learning
OpenCV
Optical character recognition
Optical flow
OPTICS algorithm
Ordered logit
Ordered probit
Ordinal regression
Ordination (statistics)
Ordinary least squares
Orthogonalization
Outline of object recognition
Overfitting
Part-based models
Partial least squares path modeling
Partial least squares regression
Path analysis (statistics)
Path coefficient
Pattern recognition
Perceptron
Pinhole camera model
Pivot element
Plate notation
Platt scaling
Point distribution model
Point set registration
Poisson regression
Polynomial interpolation
Polynomial regression
Pose (computer vision)
Power iteration
Precision and recall
Predictive modelling
Preference learning
Principal component analysis
Prior knowledge for pattern recognition
Probabilistic classification
Probabilistic neural network
Probabilistic programming
Probably approximately correct learning
Probit model
Pruning (decision trees)
Pseudo-random number sampling
PyTorch
QR algorithm
QR decomposition
Quadratic classifier
Quadratic sieve
Quantile regression
Quantum machine learning
Quantum neural network
Rademacher complexity
Radial basis function
Radial basis function kernel
Radial basis function network
Random effects model
Random forest
Random geometric graph
Random projection
Random sample consensus
Random subspace method
Randomized Hough transform
Receiver operating characteristic
Reciprocity (network science)
Rectifier (neural networks)
Recurrent neural network
Recursive Bayesian estimation
Recursive neural network
Recursive partitioning
Region of interest
Regression analysis
Regularization (mathematics)
Regularized least squares
Rejection sampling
Relation network
Relevance vector machine
Representer theorem
Reservoir computing
Residual neural network
Restricted Boltzmann machine
Ridge detection
Root-mean-square deviation
Row echelon form
Rubin causal model
Rule-based machine learning
Runge's phenomenon
Sammon mapping
Sample complexity
Scale space
Scale space implementation
Scale-free network
Scale-invariant feature transform
Scale-space axioms
Scale-space segmentation
Scikit-learn
Segmentation-based object categorization
Segmented regression
Self-organizing map
Semi-supervised learning
Semidefinite embedding
Semiparametric regression
Sequence labeling
Sequential pattern mining
Seq2seq
Shape context
Shattered set
Single-linkage clustering
Short division
Siamese neural network
Sieve of Atkin
Sieve of Eratosthenes
Sigmoid function
Silhouette (clustering)
Similarity (network science)
Similarity learning
Similarity measure
Simple linear regression
Small-world network
Smoothing spline
Softmax function
Solovay–Strassen primality test
Sparse dictionary learning
Sparse distributed memory
Sparse matrix
Sparse network
Spatial network
Spectral clustering
Speech processing
Speech recognition
Speech synthesis
Speeded up robust features
Spike-and-slab regression
Spiking neural network
Spline interpolation
Stability (learning theory)
Statistical classification
Statistical learning theory
Stochastic block model
Structural equation modeling
Structural risk minimization
Structure from Motion
Structure mining
Structure tensor
Structured prediction
Structured support vector machine
Subgraph isomorphism problem
Supervised learning
Support vector machine
Synaptic weight
Synthetic media
T-distributed stochastic neighbor embedding
TensorFlow
Text mining
Theano (software)
Thresholding (image processing)
Tikhonov regularization
Time delay neural network
Topological data analysis
Total least squares
Total operating characteristic
Training, test, and validation sets
Transduction (machine learning)
Transfer learning
Transformer (machine learning model)
Trial division
Triangulation (computer vision)
Tridiagonal matrix
Triplet loss
TrustRank
Types of artificial neural networks
Uncertain inference
Universal approximation theorem
Unsupervised learning
Vanishing gradient problem
Vapnik–Chervonenkis theory
Variable-order Markov model
Variance function
Variance reduction
Variational Bayesian methods
VC dimension
Vector quantization
Video content analysis
Video tracking
Visual descriptor
Visual odometry
Viterbi algorithm
Wake-sleep algorithm
Watts–Strogatz model
Weak supervision
Weighted least squares
Wheel factorization
Winner-take-all (computing)
Winnow (algorithm)
XGBoost
Zero-shot learning