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- modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly...
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- solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed...
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- large range of problems, including text classification [Joachims, 1999c][Joachims, 1998a], image recognition tasks, bioinformatics...
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- Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation , Normal Mixture Modeling fitted ... algorithm for Model-Based Clustering, Classification, and Density Estimation, including Bayesian regularization...
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- Additionally, as each problem includes a specific classification that is designed to be useful...
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- programming relaxations. It also provides automatic constraint classification, preprocessing, primal heuristics and constraint generation. Moreover...
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- attractive framework for the design of pattern classification systems. A new constructive neural network learning ... favorably with other learning algorithms for pattern classification...
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- nonlinear modelling, statistical tests, time series analysis, classification, clustering, etc. Please consult the R project...
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- problems of choice or of multi criteria classification on set A of actions. It constructs...
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- described. The algorithm is based on the classification of objective functions. At each iteration ... allowed to change freely. According to the classification, a new (multiobjective) optimization problem is formed...
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- image retrieval system, which uses semantics classification methods, a wavelet-based approach for feature extraction...
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- SimpleMKL can be applied beyond binary classification, for problems like regression, clustering (one-class classification ... multiclass classification. Experimental results show that the proposed algorithm converges rapidly and that its efficiency ... some model selection problems related to multiclass classification problems...
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- problems of various kinds including as regression, classification, unsupervised learning, etc. It includes evolutionary learning...
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- Applications of CRACK in the classification of integrable systems. The classifications of integrable systems...
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- fast scalable classifier for data mining. Classification is an important problem in the emerging field ... data mining. Although classification has been studied extensively in the past, most of the classification ... breadth-first tree growing strategy to enable classification of disk-resident datasets. SLIQ also uses...
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- Breiman and Cutler’s random forests for classification and regression. Classification and regression based...
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- under the ROC curve for multiple class classification problems The area under the ROC curve ... widely used measure of performance of supervised classification rules. It has the attractive property that...
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- SSVM: A smooth support vector machine for classification. Smoothing methods, extensively used for solving important ... support vector machine for pattern classification using a completely arbitrary kernel. We term such reformulation...
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- scalable parallel classifier for data mining. Classification is an important data mining problem. Although classification ... well-studied problem, most of the current classification algorithms require that all or a portion ... present a new decision-tree-based classification algorithm, called SPRINT that removes...