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
- Approximate Bayesian computation
- Arnoldi iteration
- Artificial neural network
- Artificial neuron
- Association rule learning
- Assortativity
- Attack tolerance
- 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 interpretation of kernel regularization
- 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
- Canny edge detector
- 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
- Co-training
- 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
- Connected-component labeling
- Consensus clustering
- Constellation model
- Constrained conditional model
- Continuous-time Markov chain
- Convergent matrix
- Convolutional neural network
- Corner detection
- Correlation clustering
- Correspondence analysis
- Correspondence problem
- Cosine similarity
- Cross-validation (statistics)
- Curse of dimensionality
- Curve fitting
- Data augmentation
- 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
- Design matrix
- Determining the number of clusters in a data set
- Differentiable neural computer
- Diffusion map
- Diffusion model
- 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
- Evolution of a random network
- 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 primality test
- Fermat's factorization method
- Fine-tuning (deep learning)
- Fixed effects model
- Flow-based generative model
- Foreground detection
- Forward algorithm
- Forward–backward algorithm
- Fréchet inception distance
- 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
- Generalised Hough transform
- Generalization error
- Generalized additive model
- Generalized Hebbian algorithm
- Generalized least squares
- Generalized linear model
- Generative adversarial networks
- Generative model
- Generative_pre-trained_transformer
- Generative topographic map
- Gesture recognition
- Giant component
- Gibbs sampling
- Givens rotation
- Google JAX
- Gradient boosting
- Gram–Schmidt process
- Graph cuts in computer vision
- Graph edit distance
- Graph isomorphism problem
- Graph matching
- Graphical model
- Grid method multiplication
- Group method of data handling
- Growth function
- Gröbner basis
- Hamiltonian Monte Carlo
- 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
- Inception score
- Incremental learning
- Inductive bias
- Influence diagram
- Information gain in decision trees
- Instance selection
- Instance-based learning
- 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 methods for vector output
- Kernel perceptron
- Kernel principal component analysis
- Kernel regression
- Knowledge distillation
- Kriging
- Krylov subspace
- Labeled data
- Lancichinetti–Fortunato–Radicchi benchmark
- Lanczos algorithm
- Language model
- Large_language_model
- Large width limits of neural networks
- Lasso (statistics)
- Latent class model
- Latent growth modeling
- Latent variable model
- Lattice multiplication
- Layer (deep learning)
- Lazy learning
- Leakage (machine 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
- Mamba_(deep_learning_architecture)
- 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
- Matching pursuit
- Mathematics of artificial neural networks
- 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
- Mixture_of_experts
- 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 kernel density estimation
- Multivariate probit model
- Naive Bayes classifier
- Neighborhood operation
- Network controllability
- Network motif
- Network science
- Network theory
- Neural architecture search
- Neural network Gaussian process
- Neural_scaling_law
- Neural tangent kernel
- Neural Turing machine
- Newton polynomial
- Node deletion
- Non-linear least squares
- Non-negative matrix factorization
- Nonlinear dimensionality reduction
- Nonlinear mixed-effects model
- 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
- Ordinary least squares
- Ordination (statistics)
- Orthogonalization
- Outline of object recognition
- Overfitting
- Part-based models
- Partial least squares path modeling
- Partial least squares regression
- Particle filter
- 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
- Prewitt operator
- Principal component analysis
- Principal component regression
- Prior knowledge for pattern recognition
- Probabilistic classification
- Probabilistic neural network
- Probabilistic programming
- Probably approximately correct learning
- Probit model
- Projection pursuit
- Projection pursuit regression
- Proximal gradient methods for learning
- 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
- Ridge function
- Robustness of complex networks
- 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
- Seq2seq
- Sequence labeling
- Sequential pattern mining
- Shape context
- Shattered set
- 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
- Single-linkage clustering
- Small-world network
- Smoothing spline
- Sobel operator
- Softmax function
- Solovay–Strassen primality test
- Sora (text-to-video model)
- Sparse approximation
- 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
- Strassen algorithm
- 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
- Text-to-image_model
- Text-to-video model
- 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
- Trilinear interpolation
- 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 autoencoder
- Variational Bayesian methods
- VC dimension
- Vector quantization
- Video content analysis
- Video tracking
- Vision_transformer
- 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