Automatic classification of digital signal types is extremely important in communication intelligence. In this paper, we present a highly efficient digital signal type classifier for fading environments. In the proposed method, an equalizer is used to reduce the channel effects. Selected combination of the fourth, the sixth and the eighth order of moments and cumulants of the received signal ar...

Error correcting output codes (ECOC) have been proposed to enhance generalization ability of classifiers. If, instead of discrete error functions, continuous error functions are used, unclassifiable regions of multiclass support vector machines are resolved. In this paper, we discuss minimum operations as well as average operations for error functions of support vector machines and show the equ...

In this paper, we present and evaluate a novel method for feature selection for Multiclass Support Vector Machines (MSVM). It consists in determining the relevant features using an upper bound of generalization error proper to the multiclass case called the multiclass radius margin bound. A score derived from this bound will rank the variables in order of relevance, then, forward method will be...

Kernelized Support Vector Machines (SVM) have gained the status of off-the-shelf classifiers, able to deliver state of the art performance on almost any problem. Still, their practical use is constrained by their computational and memory complexity, which grows super-linearly with the number of training samples. In order to retain the low training and testing complexity of linear classifiers an...

At first, support vector machines (SVMs) were applied to solve binary classification problems. They can also be extended to solve multicategory problems by the combination of binary SVM classifiers. In this paper, we propose a new fuzzy model that includes the advantages of several previously published methods solving their drawbacks. For each datum, a class is rejected using information provid...

Since support vector machines for pattern classification are based on two-class classification problems, unclassifiable regions exist when extended to problems with more than two classes. In our previous work, to solve this problem, we developed fuzzy support vector machines for one-against-all and pairwise classifications, introducing membership functions. In this paper, for one-against-all cl...

Since support vector machines for pattern classification are based on two-class classification problems, unclassifiable regions exist when extended to n (> 2)-class problems. In our previous work, to solve this problem, we developed fuzzy support vector machines for oneto-(n−1) classification. In this paper, we extend our method to pairwise classification. Namely, using the decision functions o...

The multiclass classification problem is considered and resolved through coding and regression. There are various coding schemes for transforming class labels into response scores. An equivalence notion of coding schemes is developed, and the regression approach is adopted for extracting a low-dimensional discriminant feature subspace. This feature subspace can be a linear subspace of the colum...

In this paper, an importance sampling method – cross entropy method is presented to deal with solving support vector machines (SVM) problem for multiclass classification cases. Using one-against-rest (OAR) and one-against-one (OAO) approaches, several binary svm classifiers are constructed and combined to solve multiclass classification problems. For each binary SVM classifier, the cross entrop...

In this paper, we present the mathematical foundations of a probabilistic neural network for gene selection and classification of high-dimensional microarray data. We present a catalogue of features that a classification system for microarray data should incorporate. We then use this catalogue and compare the theoretical properties of probabilistic neural networks with support vector machines w...