| |
| Accept-Reject Sampling Method | |
| Accumulated Error Backpropagation | |
| |
| |
| |
| |
| |
| Adaptive Bitrate Algorithm | |
| |
| Adaptive Gradient Algorithm | |
| Adaptive Moment Estimation Algorithm | |
| Adaptive Resonance Theory | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| Artificial Neural Network | |
| |
| |
| |
| |
| |
| |
| Automatic Differentiation | |
| |
| |
| Back Propagation Algorithm | |
| Back Propagation Through Time | |
| |
| |
| |
|
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
|
|
| |
| |
| |
| |
| |
| |
| |
| Between-Class Scatter Matrix | |
| |
| |
| |
| |
| |
| Bias-Variance Decomposition |
|
| |
| Bidirectional Recurrent Neural Network | |
| |
| Bilingual Evaluation Understudy | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
|
|
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| Classification And Regression Tree | |
| |
|
|
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| Computational Learning Theory | |
| |
| |
| Conditional Probability Distribution |
|
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
|
|
| |
| |
| |
| |
| |
| Convex Quadratic Programming | |
| |
| |
| Convolutional Neural Network | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| Deep Convolutional Generative Adversarial Network | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| Distributed Representation | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| Empirical Conditional Entropy | |
| |
| |
| |
| |
| Empirical Risk Minimization | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| Error Backpropagation Algorithm | |
| |
| Error Correcting Output Codes | |
| |
| Error-Ambiguity Decomposition | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| Exponential Loss Function | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| Feedforward Neural Network | |
| |
| |
| |
| |
| |
| |
| Forward Stagewise Algorithm | |
| Fractionally Strided Convolution | |
| |
| |
| |
| |
|
| |
| |
| |
| |
| |
| |
| |
| |
| |
| Generalization Error Bound | |
| |
| Generalized Lagrange Function | |
| |
| Generalized Rayleigh Quotient | |
|
Generative Adversarial Network | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| Gradient Exploding Problem |
|
| |
| Graph Convolutional Network | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
|
|
| |
| |
| |
| |
| Hyperparameter Optimization | |
| |
| |
| |
| |
| |
| |
| Improved Iterative Scaling | |
| |
| Independent and Identically Distributed |
|
| |
| |
| |
| |
| |
| Inductive Logic Programming | |
| |
| |
| |
| |
| |
| |
| |
|
|
| |
| |
| |
| Joint Probability Distribution | |
| |
| |
| Karush-Kuhn-Tucker Condition | |
| |
| |
| |
| |
| Kernelized Linear Discriminant Analysis | |
| |
|
|
| |
| |
| |
| |
| |
| |
| |
| Latent Dirichlet Allocation | |
| |
| |
| |
| |
| |
|
|
| |
| |
| |
| |
| Learning Vector Quantization | |
| |
| Least Squares Regression Tree | |
| |
| |
| Linear Chain Conditional Random Field | |
| Linear Classification Model | |
| |
| |
| Linear Discriminant Analysis |
|
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
|
|
| |
| Long Short-Term Memory Network | |
| |
| |
| Low Rank Matrix Approximation | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| Maximum Likelihood Estimation | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| Minimal Description Length | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| Multi-Class Classification | |
| |
| Multi-Head Self-Attention | |
| |
| |
| Multi-Layer Feedforward Neural Networks | |
| |
| |
| Multiple Dimensional Scaling | |
| Multiple Linear Regression | |
| |
| Multivariate Normal Distribution | |
| |
| |
| |
| |
| Nearest Neighbor Classifier | |
| |
| Neighbourhood Component Analysis | |
| |
| |
| |
| |
| |
| |
| Noise-Contrastive Estimation | |
| |
| |
| |
| Non-Negative Matrix Factorization | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| Parallel Distributed Processing | |
| |
| |
| |
| |
| |
| |
| |
| Partially Observable Markov Decision Processes | |
| |
| |
| |
| |
| |
| |
| |
| |
| Polynomial Kernel Function | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| Principal Component Analysis | |
| |
| Probabilistic Context-Free Grammar | |
| Probabilistic Graphical Model | |
| |
| Probability Density Function | |
| |
| Probably Approximately Correct | |
| |
| Prototype-Based Clustering | |
| Proximal Gradient Descent | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| Reproducing Kernel Hilbert Space | |
| |
| |
| |
| |
| Restricted Boltzmann Machine | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| Semi-Definite Programming | |
| Semi-Naive Bayes Classifiers | |
| Semi-Restricted Boltzmann Machine | |
| Semi-Supervised Clustering | |
| |
| Semi-Supervised Support Vector Machine | |
| |
| |
| |
| |
| |
| |
| |
| |
| Simultaneous Localization And Mapping | |
| |
| Singular Value Decomposition | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| Standard Normal Distribution | |
| |
| |
| State-Action Value Function | |
| |
| |
| |
| |
| Stochastic Gradient Descent | |
| |
| |
| |
| |
| |
| Structural Risk Minimization | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| Temporal-Difference Learning | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| Time Delay Neural Network | |
| Time Homogenous Markov Chain | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
|
|
| Transductive Transfer Learning | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| Undirected Graphical Model | |
| |
|
|
| |
| Universal Approximation Theorem | |
| |
| Universal Function Approximator | |
| |
| Unsupervised Layer-Wise Training | |
| |
| |
| |
| |
| |
| |
| Value Function Approximation | |
|
|
| Vanishing Gradient Problem | |
| Vapnik-Chervonenkis Dimension | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| Weakly Supervised Learning | |
| |
| |
| |
| |
| |
| |
| Within-Class Scatter Matrix | |
| |
| Word Sense Disambiguation | |
| |
| |
| |
| |