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| - | ====== Catalog of methods in AI/ | ||
| - | |||
| - | This is a classification of techniques and algorithms, giving only keywords ... | ||
| - | |||
| - | ===== Learning ===== | ||
| - | ==== Supervised learning ==== | ||
| - | === Classification === | ||
| - | |||
| - | * **MLP (Multi Layers Perceptron)** - //PMC (Perceptron multicouches)// | ||
| - | * __gradient backpropagation__ - // | ||
| - | * __stochastic__ | ||
| - | * __with inertia__ | ||
| - | * __simulated annealing__ - //recuit simulé// | ||
| - | * __newton__ (second order) | ||
| - | * **RBFNN (Radial Basis Functions Neural Networks)** | ||
| - | * __k-means then gradient descent__ | ||
| - | * __incremental addition of neurons then exact method__ | ||
| - | * **SVM (Support Vectors Machine)** | ||
| - | * **Decision tree** - //arbre de décision// | ||
| - | * __ID3__ (based on entropy) | ||
| - | * __**k-nearest neighbors**__ - //k plus proches voisins// | ||
| - | * **Boosting** | ||
| - | * __AdaBoost__ (ADAptive BOOSTing) | ||
| - | * __Discrete AdaBoost__ | ||
| - | * __Real AdaBoost__ | ||
| - | * __Gentle AdaBoost__ | ||
| - | * __LogitBoost__ | ||
| - | * __FloatBoost__ | ||
| - | * __AdaBoost.Reg__ | ||
| - | * __Multiclass AdaBoost.M1__ | ||
| - | * __Multiclass AdaBoost.M2__ | ||
| - | * __Multilabel AdaBoost.MR__ | ||
| - | * __Multilabel AdaBoost.MH__ | ||
| - | * __SAMME__ (Stagewise Additive Modeling using a Multi-class Exponential loss function) | ||
| - | * __GAMBLE__ (Gentle Adaptive Multiclass Boosting Learning) | ||
| - | * __LPBoost__ (Linear Programming BOOSTing) | ||
| - | * __TotalBoost__ | ||
| - | * RotBoost | ||
| - | * alphaBoost | ||
| - | * CGBoost (Conjugate Gradient BOOSTing) | ||
| - | * __Bootstrap Aggregating__ | ||
| - | * **Cascades of detectors** [[classification# | ||
| - | * **Trees of detectors** | ||
| - | |||
| - | === Regression === | ||
| - | |||
| - | * **MLP (Multi Layers Perceptron)** | ||
| - | * **RBFNN (Radial Basis Functions Neural Network)** | ||
| - | * **SVR (Support Vectors Regressor)** | ||
| - | |||
| - | === Pattern recognition === | ||
| - | |||
| - | * __**Viola-Jones Detector**__ | ||
| - | * with extended set of haar features | ||
| - | * Rotation Invariant | ||
| - | |||
| - | ==== Unsupervised learning ==== | ||
| - | === Vector quantization / Clustering === | ||
| - | |||
| - | * __**Sequential leader**__ | ||
| - | * __**k-means**__ - // | ||
| - | * __**GNG (Growing Neural Gas)**__ | ||
| - | * __**Auto-organizing maps (Kohonen)**__ - //cartes auto-organisatrices de Kohonen// | ||
| - | |||
| - | |||
| - | ==== Reinforcement learning ==== | ||
| - | |||
| - | * **MDP (Markov Decision Processes)** | ||
| - | * __Q-learning__ | ||
| - | * __Value iteration__ | ||
| - | * __Policy iteration__ | ||
| - | |||
| - | ===== Planification ===== | ||
| - | |||
| - | ==== Symbolic ==== | ||
| - | === State space search === | ||
| - | |||
| - | * __A*__ | ||
| - | * __WA*__ | ||
| - | * __IDA*__ | ||
| - | * __Dijkstra__ | ||
| - | |||
| - | === Logics === | ||
| - | |||
| - | * __**GraphPlan**__ | ||
| - | * __Stan__ | ||
| - | * __IPP__ | ||
| - | * __SGP__ | ||
| - | * __**SATplan (SATisfiability PLANning)**__ | ||
| - | |||
| - | ==== Others ==== | ||
| - | |||
| - | * **Genetic algorithms** - // | ||
| - | * **Ant colonies** - //colonies de fourmis// | ||
| - | |||
| - | ==== Specific ==== | ||
| - | === Path planning === | ||
| - | |||
| - | * **Configurations space** | ||
| - | * **Potential fields** | ||
| - | |||
| - | ===== Perception ===== | ||
| - | ==== Vision ==== | ||
| - | |||
| - | === Color Quantization === | ||
| - | |||
| - | * **RGB cone** | ||
| - | * **YUV polygon** | ||
| - | * **HSV rectangle** | ||
| - | * **Lab** | ||
| - | |||
| - | === Image segmentation === | ||
| - | |||
| - | * **Floodfill** - // | ||
| - | * **Watershed** - //lignes de partage des eaux// | ||
| - | |||
| - | === Filters === | ||
| - | |||
| - | * Anti-noise (smoothing) | ||
| - | * __Median Filter__ - //filtre médian// | ||
| - | * __Vector Median Filter__ | ||
| - | * __Kuwahara filter__ | ||
| - | * __Peer Group Filtering__ | ||
| - | * __Anisotropic Filtering__ | ||
| - | * Gradient | ||
| - | * __Prewitt__ [[vision# | ||
| - | * __Roberts__ | ||
| - | * __Sobel__ | ||
| - | * __Laplace__ | ||
| - | * __Scharr__ | ||
| - | * Morphological | ||
| - | * __dilation__ - // | ||
| - | * __erosion__ - // | ||
| - | * __opening__ - // | ||
| - | * __closing__ - // | ||
| - | |||
| - | === Edge detection === | ||
| - | |||
| - | * __**Canny detector**__ | ||
| - | * __Canny-Deriche__ | ||
| - | |||
| - | === Pattern recognition === | ||
| - | |||
| - | * __**Mean-Square Regression**__ - // | ||
| - | * **Hough Transforms** | ||
| - | * __Standard Hough Transform__ | ||
| - | * __Randomized Hough Transform__ | ||
| - | * __Connective Randomized Hough Transform__ | ||
| - | * __Combinatorial Hough Transform__ | ||
| - | * __Adaptive Hough Transform__ | ||
| - | * __Probabilistic Hough Transform__ | ||
| - | * __Adaptive Probabilistic Hough Transform__ | ||
| - | * __Progressive Probabilistic Hough Transform__ | ||
| - | * __Hierarchical Hough Transform__ | ||
| - | * __Sampling Hough Transform__ | ||
| - | * **Generalized Hough Transform** | ||
| - | * **UpWrite method** | ||
| - | * **Curvogram** | ||
| - | |||
| - | === Tracking === | ||
| - | |||
| - | * __**Kalman Filter**__ | ||
| - | * Generalized Kalman Filter | ||
| - | * **Correlation Tracking** | ||
| - | * __ACA__ (Area Correlation Algorithm) | ||
| - | * __KLT Tracker__ (Kanade-Lucas-Tomasi) | ||
| - | * __IPAN Tracker__ | ||
| - | * __MeanShift__ | ||
| - | * Features detection | ||
| - | * __Harris detector__ | ||
| - | * __Susan detector__ | ||
| - | * __Multiresolution Contrast detector__ | ||
| - | |||
| - | ==== Sensors fusion ==== | ||
| - | |||
| - | * __**Kalman filter**__ | ||
| - | * **Particles filter** (bayesian network) - //filtrage particulaire// | ||
