In this paper the data classification technique, implying the consistent application of the SVM and Parzen classifiers, has been suggested. The Parser classifier applies to data which can be both correctly and erroneously classified using the SVM classifier, and are located in the experimentally defined subareas near the hyperplane which separates the classes. A herewith, the SVM classifier is used with the default parameters values, and the optimal parameters values of the Parser classifier are determined using the genetic algorithm. The experimental results confirming the effectiveness of the proposed hybrid intellectual data classification technology have been presented
Among numerous artificial intelligence approaches, k-Nearest Neighbor algorithms, genetic algorithms...
Several new computational intelligence methods and their applications are investigated in this thesi...
Support vector machines are relatively new approach for creating classifiers that have become increa...
In this paper the data classification technique, implying the consistent application of the SVM and ...
Data Mining has been found to be the most active fields of research for the concluding couple of dec...
This book delivers a definite and compact knowledge on how hybridization can help improving the qual...
Support Vector Machines (SVM) method is a powerful classification technique as a data mining applica...
Hybrid methods are very important for feature selection in case of the classification of high-dimens...
In pattern classification, feature selection is an important factor in the performance of classi-fie...
Abstract: This paper discusses a genetic implementation of the growing hyperspheres classifier (GHS)...
Support vector machines (SVMs) were originally formulated for the solution of binary classification ...
This book provides a unified framework that describes how genetic learning can be used to design pat...
We propose a novel learning technique for classification as result of the hybridization between supp...
Developing an accurate classifier for high dimensional microarray datasets is a challenging task due...
This research is to develop a biologically inspired hybrid intelligent system - evolving neural netw...
Among numerous artificial intelligence approaches, k-Nearest Neighbor algorithms, genetic algorithms...
Several new computational intelligence methods and their applications are investigated in this thesi...
Support vector machines are relatively new approach for creating classifiers that have become increa...
In this paper the data classification technique, implying the consistent application of the SVM and ...
Data Mining has been found to be the most active fields of research for the concluding couple of dec...
This book delivers a definite and compact knowledge on how hybridization can help improving the qual...
Support Vector Machines (SVM) method is a powerful classification technique as a data mining applica...
Hybrid methods are very important for feature selection in case of the classification of high-dimens...
In pattern classification, feature selection is an important factor in the performance of classi-fie...
Abstract: This paper discusses a genetic implementation of the growing hyperspheres classifier (GHS)...
Support vector machines (SVMs) were originally formulated for the solution of binary classification ...
This book provides a unified framework that describes how genetic learning can be used to design pat...
We propose a novel learning technique for classification as result of the hybridization between supp...
Developing an accurate classifier for high dimensional microarray datasets is a challenging task due...
This research is to develop a biologically inspired hybrid intelligent system - evolving neural netw...
Among numerous artificial intelligence approaches, k-Nearest Neighbor algorithms, genetic algorithms...
Several new computational intelligence methods and their applications are investigated in this thesi...
Support vector machines are relatively new approach for creating classifiers that have become increa...