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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...
We propose a proximal classification method, named as the hyperspherical 2-surface proximal (H2SP) classifier, by seeking the two smallest hyperspheres for the positive class and the negative class, respectively, each containing the most samples from one class while also the least samples from the other. The proposed H2SP classifier is validated using five public benchmark datasets, including one...
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...
This paper proposes a novel method for breast cancer diagnosis using the feature generated by genetic programming (GP) based on Fisher criterion. GP as an evolutionary mechanism provides a training structure to generate features. Fisher criterion is employed to help GP optimize features whose values corresponding to pattern vector belonging to the same class are extremely similar while those corresponding...
The feature extraction is one of the major challenges for the pattern recognition. This helps to maximise the useful information from the raw data in order to make the classification effective and simple. In this paper, one of the machine learning approaches, genetic programming (GP), is employed to extract features from the raw vibration data taken from a rotating machine with several different conditions...
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