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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...
- Referenced in 180 articles
- 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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- Additionally, as each problem includes a specific classification that is designed to be useful...
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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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- 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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- Breiman and Cutler’s random forests for classification and regression. Classification and regression based...
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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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- 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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- 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...
- Referenced in 83 articles
- problems of various kinds including as regression, classification, unsupervised learning, etc. It includes evolutionary learning...
- Referenced in 47 articles
- 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...
- Referenced in 80 articles
- image retrieval system, which uses semantics classification methods, a wavelet-based approach for feature extraction...
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- open source library for large-scale linear classification. It supports logistic regression and linear support...
- Referenced in 56 articles
- Applications of CRACK in the classification of integrable systems. The classifications of integrable systems...
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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...