We describe an efficient technique to weigh word-based features in binary classification tasks and show that it significantly improves classification accuracy on a range of proble...
Justin Martineau, Tim Finin, Anupam Joshi, Shamit ...
In this paper, we present a novel semidefinite programming approach for multiple-instance learning. We first formulate the multipleinstance learning as a combinatorial maximum marg...
The main purpose of this paper is to compare the support vector machine (SVM) developed by Vapnik with other techniques such as Backpropagation and Radial Basis Function (RBF) Net...
Empirical evidence indicates that the training time for the support vector machine (SVM) scales to the square of the number of training data points. In this paper, we introduce the...
Support vector machines have gotten wide acceptance for their high generalization ability for real world applications. But the major drawback is slow training for classification p...
This paper proposes a modelling of Support Vector Machine (SVM) learning to address the problem of learning with sloppy labels. In binary classification, learning with sloppy labe...
Support vector machine (SVM) is a new learning method developed in recent years based on the foundations of statistical learning theory. By taking a transductive approach instead ...
Enabling computer systems to recognize facial expressions and infer emotions from them in real time presents a challenging research topic. In this paper, we present a real time ap...
In this paper, we propose a novel approach for the robust detection and classification of dissolve sequences in videos. Our approach is based on the multi-resolution representati...
In this paper we describe an approach for inferring the body posture using a 3D visual-hull constructed from a set of silhouettes. We introduce an appearance-based, view-independe...