In real-world recognition/classification tasks, limited by various objective factors, it is usually
difficult to collect training samples to exhaust all classes when training a recognizer or …

It is often desirable to be able to recognize when inputs to a recognition function learned in a
supervised manner correspond to classes unseen at training time. With this ability, new …

Abstract The goal of Multiple Kernel Learning (MKL) is to combine kernels derived from
multiple sources in a data-driven way with the aim to enhance the accuracy of a target kernel …

Support vector machine (SVM) has been one of the most popular learning algorithms, with
the central idea of maximizing the minimum margin, ie, the smallest distance from the …

This paper proposes a new method to design a balanced classifier on imbalanced training
data based on margin distribution theory. Recently, Large margin Distribution Machine …

Support Vector Machine (SVM) has always been one of the most successful learning
algorithms, with the central idea of maximizing the minimum margin, ie, the smallest distance …

There are two common challenges in multi-label learning (MLL), complex label relation and
imbalanced class. Few studies have focused on addressing both problems at the same time …

Deep neural networks and Multiple Kernel Learning are representation learning
methodologies of widespread use and increasing success. While the former aims at learning …

This paper develops cost-sensitive margin distribution learning and proposes Large Cost-
Sensitive margin Distribution Machine (LCSDM) to get balanced detection rate on …

Support vector machines (SVMs) and Boosting are possibly the two most popular learning
approaches during the past two decades. It is well known that the margin is a fundamental …