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We propose two variations of the support vector data description (SVDD) with negative samples (NSVDD) that learn a closed spherically shaped boundary around a set of samples in the target class by involving different forms of slack vectors, including the two-norm NSVDD and nu-NSVDD. We extend the NSVDDs to solve the multiclass classification problems based on the distances between the samples and...
Adaptive modulation schemes have been proposed to optimize Shannon's channel capacity in recent orthogonal frequency division multiplexing (OFDM) based broadband wireless standard proposals. By adapting the modulation type (effectively changing the number of bits per symbol) at the transmitter end one can improve the bit error rate (BER) during transmission at designated SNR. Blind detection of the...
Four new features for the analysis of breast masses are presented. These features were designed to be insensitive to the exact shape of the contour of the masses, so that an approximate contour, such as one extracted via an automated segmentation algorithm, can be employed in their computation. The features measure the degree of spiculation of a mass and the local fuzziness of the mass margins. The...
In this paper, a new radius based clustering algorithm is proposed. Its main objective is to map the distributions of the data by utilising the premise that clusters are determined by a distance parameter, without any specification of the number of clusters. The proposed clustering algorithm is enhanced by a reliable validity index to choose the best clustering result for the given input parameter...
Performance of support vector machines (SVM) is sensitive to the setting of kernel and regularization parameters. Hence, parameter selection becomes an important challenge that the SVM users need to face. In this paper, it is shown that the multiple parameter tuning for a 2-norm SVM (L2-SVM) classifier could be viewed as an identification problem of a nonlinear dynamic system, which could be solved...
This paper proposes development of support vector machines (SVMs) for detection and classification of rolling-element bearing faults. The training of the SVMs is carried out using the sequential minimal optimization (SMO) algorithm. In this paper, a mechanism for selecting adequate training parameters is proposed. This proposal makes the classification procedure fast and effective. Various scenarios...
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