Many classification algorithms achieve poor generalization accuracy on “noisy” data sets. We introduce a new non-convex boosting algorithm BrownBoost-δ, a noiseresistant booster, that is able to significantly increase accuracy on a set of noisy classification problems. Our algorithm consistently outperforms the original BrownBoost algorithm, AdaBoost, and LogitBoost on simulated and real data. ...

Modern communication systems require robust, adaptable and high performance decoders for efficient data transmission. Support Vector Machine (SVM) is a margin based classification and regression technique. In this paper, decoding of Bose Chaudhuri Hocquenghem codes has been approached as a multi-class classification problem using SVM. In conventional decoding algorithms, the procedure for decod...

A problem arises in data mining, when classifying unbalanced datasets using Support Vector Machines. Because of the uneven distribution and the soft margin of the classifier, the algorithm tries to improve the general accuracy of classifying a dataset, and in this process it might misclassify a lot of weakly represented classes, confusing their class instances as overshoot values that appear in...

In this paper, the authors discuss a new synthesis approach to train associative memories, based on recurrent neural networks (RNNs). They propose to use soft margin training for associative memories, which is efficient when training patterns are not all linearly separable. On the basis of the soft margin algorithm used to train support vector machines (SVMs), the new algorithm is developed in ...

A problem arises in data mining, when classifying unbalanced datasets using Support Vector Machines. Because of the uneven distribution and the soft margin of the classifier, the algorithm tries to improve the general accuracy of classifying a dataset, and in this process it might misclassify a lot of weakly represented classes, confusing their class instances as overshoot values that appear in...