Titlebook: Machine Learning and Data Mining in Pattern Recognition; 6th International Co Petra Perner Conference proceedings 2009 Springer-Verlag Berl

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Optimal Double-Kernel Combination for Classificationomplicated data sets. In this paper, a novel optimal double-kernel combination (ODKC) method is proposed for complicated classification tasks. Firstly, data sets are mapped by two basic kernels into different feature spaces respectively, and then three kinds of optimal composite kernels are construc

大酒杯

Efficient AdaBoost Region Classificationty .?∈?[0,1]. To turn a point classifier into a region classifier, the conformal framework is employed [11,14]. However, to apply the framework we need to design a non-conformity function. This function has to estimate the instance’s non-conformity for the point classifier used..This paper introduce

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ODDboost: Incorporating Posterior Estimates into AdaBoostpping classes. In this paper we propose a new upper generalization bound for weighted averages of hypotheses, which uses posterior estimates for training objects and is based on reduction of binary classification problem with overlapping classes to a deterministic problem. If we are given accurate p

TRUST

Ensemble Learning: A Study on Different Variants of the Dynamic Selection Approachnsists on the combination of the predictions obtained by different models in the ensemble to obtain the final ensemble prediction. The selection approach selects one (or more) models from the ensemble according to the prediction performance of these models on similar data from the validation set. Us

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