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Recently, Epsilon-Insensitive Support Vector Regression (epsiv SVR) has been introduced to solve regression and prediction problems. However, the preprocessing of data set and the selection of parameters can become a real computational burden to developer and user. Improper parameters usually lead to prediction performance degradation. In this paper, by introducing Parallel Multidimensional Step Search...
In this paper, we implement a new method for classification of biological signals in general, and use it in the animal behavior classification as an example. The forced swimming test of rats or mice is a frequently used behavioral test to evaluate the efficacy of drugs in rats or mice. Frequently used features for that evaluation are obtained through observing three states: immobility, struggling/climbing...
In this work we show that a metaheuristic, the variable neighborhood search (VNS), can be effectively used in order to improve the performance of the hardware-friendly version of the support vector machine (SVM). Our target is the implementation of the feed-forward phase of SVM on resource-limited hardware devices, such as field programmable gate arrays (FPGAs) and digital signal processors (DSPs)...
Meta-learning helps us find solutions to computational intelligence (CI) challenges in automated way. Meta-learning algorithm presented in this paper is universal and may be applied to any type of CI problems. The novelty of our proposal lies in complexity controlled testing combined with very useful learning machines generators. The simplest and the best solutions are strongly preferred and are explored...
In many traffic sign recognition system, one of the main tasks is to classify the shapes of traffic sign. In this paper, we have developed a shape-based classification model by using support vector machines. We focused on recognizing seven categories of traffic sign shapes and five categories of speed limit signs. Two kinds of features, binary image and Zernike moments, were used for representing...
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