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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...
- Referenced in 242 articles
- 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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- Breiman and Cutler’s random forests for classification and regression. Classification and regression based...
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- hints on the distribution and on possible classification schemes for hyperbolic 3-manifolds, besides giving...
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- programming relaxations. It also provides automatic constraint classification, preprocessing, primal heuristics and constraint generation. Moreover...
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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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- 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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- partitioning and regression trees. Recursive partitioning for classification, regression and survival trees. An implementation...
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- open source library for large-scale linear classification. It supports logistic regression and linear support...
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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...
- Referenced in 52 articles
- 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 88 articles
- problems of various kinds including as regression, classification, unsupervised learning, etc. It includes evolutionary learning...
- Referenced in 63 articles
- 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...
- Referenced in 48 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 81 articles
- image retrieval system, which uses semantics classification methods, a wavelet-based approach for feature extraction...