We introduce the OneClassMaxMinOver (OMMO) algorithm for the problem of one-class support vector classification. The algorithm is extremely simple and therefore a convenient choice for practitioners. We prove that in the hard-margin case the algorithm converges with O(1/ √ t) to the maximum margin solution of the support vector approach for one-class classification introduced by Schölkopf et al...

A new robust version of Support Vector Machine (SVM) based on value-at-risk (VaR) measure referred to as VaR-SVM is proposed in three closely related formulations, and relationships between those VaRSVM formulations is established. In contrast to classical SVMs (hard-margin SVM, soft-margin SVM, and ν-SVM), VaR-SVM is stable to data outliers. Computational experiments confirm that compared to ν...

The goal of this article is to investigate and suggest techniques for health condition monitoring and diagnosis using machine learning from sensor data. In particular, this article overview and discusses support vector machines methods such as hard margin and soft margin problems. In order to investigate the abnormalities and classify a large set of data an iterative Support Vector Machine algo...

Various problems in nonnegative quadratic programming arise in the training of large margin classifiers. We derive multiplicative updates for these problems that converge monotonically to the desired solutions for hard and soft margin classifiers. The updates differ strikingly in form from other multiplicative updates used in machine learning. In this paper, we provide complete proofs of conver...

Recently, there have been intensive studies of margin-based learning for automatic speech recognition (ASR). It is our believe that by securing a margin from the decision boundaries to the training samples, a correct decision can still be made if the mismatches between testing and training samples are well within the tolerance region specified by the margin. This nice property should be effecti...

We study boosting algorithms from a new perspective. We show that the Lagrange dual problems of AdaBoost, LogitBoost and soft-margin LPBoost with generalized hinge loss are all entropy maximization problems. By looking at the dual problems of these boosting algorithms, we show that the success of boosting algorithms can be understood in terms of maintaining a better margin distribution by maxim...

We have investigated the oncological outcome of 63 patients with soft-tissue sarcomas of the hand managed at three major centres in the United Kingdom. There were 44 males and 19 females with a mean age of 45 years (11 to 92). The three most common diagnoses were synovial sarcoma, clear cell sarcoma and epithelioid sarcoma. Local excision was carried out in 45 patients (71%) and amputation in 1...

This paper introduces a strategy for training ensemble classifiers by analysing boosting within margin theory. We present a bound on the generalisation error of ensembled classifiers in terms of the 2-norm of the margin slack vector. We develop an effective, adaptive and robust boosting algorithm, DMBoost, by optimising this bound. The soft margin based quadratic loss function is insensitive to...

Multi-view learning receives increasing interest in recent years to analyze complex data. Lately, multiview maximum entropy discrimination (MVMED) and alternative MVMED (AMVMED) were proposed as extensions of maximum entropy discrimination (MED) to the multi-view learning setting, which use the hard margin consistency principle that enforces two view margins to be the same. In this paper, we pr...