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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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- nonlinear modelling, statistical tests, time series analysis, classification, clustering, etc. Please consult the R project...
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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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- large range of problems, including text classification [Joachims, 1999c][Joachims, 1998a], image recognition tasks, bioinformatics...
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- Breiman and Cutler’s random forests for classification and regression. Classification and regression based...
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- Additionally, as each problem includes a specific classification that is designed to be useful...
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- competition and workshop. There are five challenges: classification, detection, segmentation, action classification, and person layout...
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- hints on the distribution and on possible classification schemes for hyperbolic 3-manifolds, besides giving...
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- problems of choice or of multi criteria classification on set A of actions. It constructs...
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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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- open source library for large-scale linear classification. It supports logistic regression and linear support...
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- programming relaxations. It also provides automatic constraint classification, preprocessing, primal heuristics and constraint generation. Moreover...
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- partitioning and regression trees. Recursive partitioning for classification, regression and survival trees. An implementation...
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- described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often...
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- Autoclass - A Bayesian Approach to Classification. We describe a Bayesian approach to the unsupervised discovery ... maximal posterior probability parameters. We rate our classifications with an approximate posterior probability ... discuss the rationale behind our approach to classification. We give the mathematical development...
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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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- CMAR: accurate and efficient classification based on multiple class-association rules. Previous studies propose that ... associative classification has high classification accuracy and strong flexibility at handling unstructured data. However ... mined rules and sometimes biased classification or overfitting since the classification is based on only ... rule. The authors propose a new associative classification method, CMAR, i.e., Classification based on Multiple...
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- learning algorithm to improve k-nearest neighbor classification by learning a generalized Euclidean metric Equation ... especially for nearest neighbor classification...