In a variety of applications, kernel machines such as Support Vector Machines (SVMs) have been used with great success often delivering stateof-the-art results. Using the kernel t...
The Support Vector Machine (SVM) of Vapnik [9] has become widely established as one of the leading approaches to pattern recognition and machine learning. It expresses predictions...
We present a novel method for clustering using the support vector machine approach. Data points are mapped to a high dimensional feature space, where support vectors are used to d...
Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladi...
The decision functions constructed by support vector machines (SVM’s) usually depend only on a subset of the training set—the so-called support vectors. We derive asymptotical...
This paper proposes a method for computing fast approximations to support vector decision functions in the field of object detection. In the present approach we are building on an...
This paper presents data selection procedures for support vector machines (SVM). The purpose of data selection is to reduce the dataset by eliminating as many non support vectors ...
This paper investigates the application of randomized algorithms for large scale SVM learning. The key contribution of the paper is to show that, by using ideas random projections...
We apply kernel-based machine learning methods to online learning situations, and look at the related requirement of reducing the complexity of the learnt classifier. Online meth...
An approach to multi-view face detection based on head pose estimation is presented in this paper. Support Vector Regression is employed to solve the problem of pose estimation. T...