We compare the performance of three support vector machine (SVM) types: weighted SVM, one-class SVM and support vector data description (SVDD) for the application of seizure detection in an animal model of chronic epilepsy. Large EEG datasets (273 h and 91 h respectively, with a sampling rate of 1 kHz) from two groups of rats with chronic epilepsy were used in this study. For each of these EEG ...

An adaptive single image superresolution (SR) method using a support vector data description (SVDD) is presented. The proposed method represents the prior on high-resolution (HR) images by hyperspheres of the SVDD obtained from training examples and reconstructs HR images from low-resolution (LR) observations based on the following schemes. First, in order to perform accurate reconstruction of ...

Deep neural network-based autoencoders can effectively extract high-level abstract features with outstanding generalization performance but suffer from sparsity of extracted features, insufficient robustness, greedy training each layer, and a lack global optimization. In this study, the broad learning system (BLS) is improved to obtain new model for data reconstruction. Support Vector Domain De...

In this paper, a novel selective ensemble strategy for support vector data description (SVDD) using the Renyi entropy based diversity measure is proposed to deal with the problem of one-class classification. In order to obtain compact classification boundary, the radius of ensemble is defined as the inner product of the vector of combination weights and the vector of the radii of SVDDs. To make...

Object detecting and tracking is an important technique used in diverse applications of machine vision, and has made great progress with the prevalence of artificial intelligence technology, among which the detecting and tracking moving object under dynamic scenes is more challenging for high requirements on real-time performance and reliability. Essentially analyzing, object detecting and trac...

Support Vector Domain Description (SVDD) is an effective method for describing a set of objects. As a basic tool, several application-oriented extensions have been developed, such as support vector clustering (SVC), SVDD-based k-Means (SVDDk-Means) and support vector based algorithm for clustering data streams (SVStream). Despite its significant success, one inherent drawback is that the descri...

Poor model generalization, missing or false alarms, and heavy dependence on expert's experience are some of the major problems which exist in traditional incipient fault detection (IFD) methods. An IFD rolling bearing application method based combination improved ? 1 trend filtering (L1TF) suppo...

These days, imbalanced datasets, denoted throughout the paper by ID, (a dataset that contains some (usually two) classes where one considerably smaller number of samples than other(s)) emerge in many real world problems (like health care systems or disease diagnosis systems, anomaly detection, fraud stream based malware detection and so on) these datasets cause under-training minority class(es)...