From a practical perspective, a novel method using multi-category support vector machines
(MC-SVM) is proposed to identify supply chain disruptions (SCD). With the data related to …

We propose a novel computational framework that integrates information-theoretic feature
selection with discrete k k-median clustering (DKM). DKM is a domain-independent …

In this paper, we consider the classification problem within the multiple instance learning
(MIL) context. Training data is composed of labeled bags of instances. Despite the large …

Support vector machine (SVM) algorithm has shown a good learning ability and
generalization ability in classification, regression and forecasting. This paper mainly …

We extend the idea of reproducing kernel Hilbert spaces (RKHS) to Banach spaces,
developing a theory of pairs of reproducing kernel Banach spaces (RKBS) without the …

This survey concerns applications of mathematical programming in the context of
classification. We mainly discuss two supervised learning methods: Support Vector …

In this paper we study standard and novel kernel based nonlinear classification methods on
a neural data recorded during a visuomotor task performed by a macaque monkey. The …

Multiple instance setting is introduced by Dietterich et al.[1] in the context of drug activity
prediction. We focus on the problem of maximum margin classification for multiple instance …

Data mining (DM) and operations research (OR) are two largely independent paradigms of
science. DM involves data driven methods which aim to extract meaningful patterns from …

This survey concerns a rapidly emerging interdisciplinary research area. In recent years,
numerous biomedical applications have benefited from optimization techniques. We present …