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The generalization error bounds found by current error models using the number of effective parameters of a classifier and the number of training samples are usually very loose. These bounds are intended for the entire input space. However, support vector machine (SVM), radial basis function neural network (RBFNN), and multilayer perceptron neural network (MLPNN) are local learning machines for solving...
Large margin classifiers have been widely applied in solving supervised learning problems. One representative model in large margin learning is the support vector machine (SVM). SVM is an unstructured classifier since the data structure information is underutilized and the decision hyperplane calculation relies exclusively on the support vectors. To incorporate the data covariance information into...
We had developed the localized generalization error model for supervised learning with minimization of mean square error. In this work, we extend the error model to single layer perceptron neural network (SLPNN). For a trained SLPNN and a given training dataset, the proposed error model bounds above the error for unseen samples which are similar to the training samples. This pilot study is the important...
In some cases, an ambiguous pattern may belong to more than one class, however it is forcibly classified to one of these classes in conventional support vector machine. Handling those ambiguous patterns in this way may loss the uncertainty information of the patterns. Therefore, we prefer to keep the uncertainty information in the ambiguous patterns. In this work, instead of two-class classification,...
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