Over the past two decades, one-class classification (OCC) becomes very popular due to its
diversified applicability in data mining and pattern recognition problems. Concerning to …

In supervised learning it has been shown that a collection of weak classifiers can result in a
strong classifier with error rates similar to those of more sophisticated methods. In …

Boosting has become very popular in computer vision, showing impressive performance in
detection and recognition tasks. Mainly off-line training methods have been used, which …

Abstract Incremental Support Vector Machines (SVM) are instrumental in practical
applications of online learning. This work focuses on the design and analysis of efficient …

Embodiments of the invention address the problem of detect ing bots in network traf? c
based on a classi? cation model learned during a training phase using machine learning …

This paper describes an application of one-class support vector machine (SVM) novelty
detection for detecting seizures in humans. Our technique maps intracranial …

The revival of deep neural networks and the availability of ImageNet laid the foundation for
recent success in highly complex recognition tasks. However, ImageNet does not cover all …

We propose a novel mobile system, called EchoTag, that enables phones to tag and
remember indoor locations without requiring any additional sensors or pre-installed …

Emerging technologies drive the ongoing transformation of Intelligent Transportation
Systems (ITS). This transformation has given rise to cybersecurity concerns, among which …

Support vector data description (SVDD) is a machine learning technique that is used for
single-class classification and outlier detection. The idea of SVDD is to find a set of support …