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| ====== Catalog of methods in AI/ | ====== Catalog of methods in AI/ | ||
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| This is a classification of techniques and algorithms, in order to give a broad view of solutions available to deal with classical problems. | This is a classification of techniques and algorithms, in order to give a broad view of solutions available to deal with classical problems. | ||
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| Other pages contain some more details about, which can be seen as memos with references to find more information (with the origin paper when possible). | Other pages contain some more details about, which can be seen as memos with references to find more information (with the origin paper when possible). | ||
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| ===== Learning ===== | ===== Learning ===== | ||
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| * ID3 (based on entropy) | * ID3 (based on entropy) | ||
| * __k-nearest neighbors__ - //k plus proches voisins// | * __k-nearest neighbors__ - //k plus proches voisins// | ||
| + | * __Boosting__ | ||
| + | * Boosting by majority | ||
| + | * AdaBoost (ADAptive BOOSTing) | ||
| + | * Discrete AdaBoost | ||
| + | * Real AdaBoost | ||
| + | * Gentle AdaBoost | ||
| + | * LogitBoost | ||
| + | * Probabilistic AdaBoost | ||
| + | * FloatBoost | ||
| + | * AdaBoost.Reg | ||
| + | * Multiclass AdaBoost.M1 | ||
| + | * Multiclass AdaBoost.M2 | ||
| + | * Multilabel AdaBoost.MR | ||
| + | * Multilabel AdaBoost.MH | ||
| + | * Multiclass AdaBoost.MO | ||
| + | * Multiclass AdaBoost.OC | ||
| + | * Multiclass AdaBoost.ECC | ||
| + | * GrPloss | ||
| + | * BoostMA | ||
| + | * AdaBoost.M1W | ||
| + | * SAMME (Stagewise Additive Modeling using a Multi-class Exponential loss function) | ||
| + | * GAMBLE (Gentle Adaptive Multiclass Boosting Learning) | ||
| + | * UBoost | ||
| + | * LPBoost (Linear Programming BOOSTing) | ||
| + | * TotalBoost (TOTALly corrective BOOSTing) | ||
| + | * RotBoost | ||
| + | * alphaBoost | ||
| + | * MILBoost (Multiple Instance Learning BOOSting) | ||
| + | * CGBoost (Conjugate Gradient BOOSTing) | ||
| + | * Bootstrap Aggregating | ||
| + | * __Cascades of detectors__ [[classification# | ||
| + | * __Trees of detectors__ | ||
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| === Regression === | === Regression === | ||
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| * __RBFNN (Radial Basis Functions Neural Network)__ | * __RBFNN (Radial Basis Functions Neural Network)__ | ||
| * __SVR (Support Vectors Regressor)__ | * __SVR (Support Vectors Regressor)__ | ||
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| + | === Pattern recognition === | ||
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| + | * __Viola-Jones Detector__ [[pattern-recognition# | ||
| + | * with Extended Set of Haar features | ||
| + | * Stumps or CART trees | ||
| + | * Rotation Invariant | ||
| + | * **Multiview** | ||
| + | * Parallel Cascades | ||
| + | * Pyramid Cascade | ||
| + | * Tree Cascade | ||
| + | * Vector Boosting | ||
| ==== Unsupervised learning ==== | ==== Unsupervised learning ==== | ||
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| ===== Planification ===== | ===== Planification ===== | ||
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| ==== Symbolic ==== | ==== Symbolic ==== | ||
| === State space search === | === State space search === | ||
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| ===== Perception ===== | ===== Perception ===== | ||
| ==== Vision ==== | ==== Vision ==== | ||
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| === Color Quantization === | === Color Quantization === | ||
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| * Peer Group Filtering | * Peer Group Filtering | ||
| * Anisotropic Filtering | * Anisotropic Filtering | ||
| - | * **Gradient** | + | * **Gradient** |
| * Prewitt [[vision# | * Prewitt [[vision# | ||
| * Roberts [[vision# | * Roberts [[vision# | ||
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| * Canny detector | * Canny detector | ||
| * Canny-Deriche | * Canny-Deriche | ||
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| === Pattern recognition === | === Pattern recognition === | ||
| * Mean-Square Regression - // | * Mean-Square Regression - // | ||
| - | * __Hough Transforms__ | + | * __Hough Transforms__ |
| * Standard Hough Transform | * Standard Hough Transform | ||
| * Randomized Hough Transform | * Randomized Hough Transform | ||
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| * Generalized Hough Transform | * Generalized Hough Transform | ||
| * UpWrite method | * UpWrite method | ||
| + | * Curvogram | ||
| + | * **Shape Descriptors** | ||
| + | * Ankerst' | ||
| + | * Chamfer matching | ||
| + | * Contour Likelihood Measurement | ||
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| === Tracking === | === Tracking === | ||
