This goal of this paper is to introduce a method for action recognition that significantly reduces the labeling process. The method involves training a separate linear support vector machine (SVM) classifier for each selected exemplar and combining the scores to form mid-level features. Our approach is trained and tested on the UCF Sports Action data set. The accuracies achieved by the combined...

We present new fingerprint classification algorithms based on two machine learning approaches: support vector machines (SVMs), and recursive neural networks (RNNs). RNNs are trained on a structured representation of the fingerprint image. They are also used to extract a set of distributed features which can be integrated in the SVMs. SVMs are combined with a new error correcting code scheme whi...

Extrapolated Vector Machines. Patrick Ha ner AT&T Labs-Research, 200 Laurel Ave, Middletown, NJ 07748 [email protected] Abstract Maximum margin classi ers such as Support Vector Machines (SVMs) critically depends upon the convex hulls of the training samples of each class, as they implicitly search for the minimum distance between the convex hulls. We propose Extrapolated Vector Machines...

This abstract presents the basic idea of a new adaptive methodology for reliability assessment using probabilistic classification vector machines (PCVMs) [1], a variant of support vector machines (SVMs) [2, 3]. The proposed method is pivoted around two principal concepts definition of an explicit failure boundary and its variability using PCVMs, and importance sampling (IS) [4–6]. The proposed ...

In this paper, we propose fuzzy linear programming support vector machines (LP-SVMs) that resolve unclassifiable regions for multiclass problems. Namely, in the directions orthogonal to the decision functions obtained by training the LP-SVM, we define membership functions. Then by the minimum or average operation for these membership functions we define a membership function for each class. We ...

Nonlinear support vector machines (SVMs) are more broadly useful than linear SVMs, as they can find more accurate predictors when the data under consideration have curved intrinsic category boundaries. Nonlinear SVM formulations based on kernels are, however, often much more expensive and data-intensive to solve than linear SVMs, and the resulting classifiers can be expensive to apply. This pap...

Due to good performance in classification and regression, support vector machines have attracted much attention and become one of the most popular learning machines in last decade. As a black box, the support vector machine is difficult for users’ understanding and explanation. In many application domains including medical diagnosis or credit scoring, understandability and interpretability are ...

Problem statement: To accept the inputs as spoken word utterances uttered by various speakers, recognize the corresponding spoken words and initiate action pertaining to that word. Approach: A novel Linear-Polynomial (LP) Kernel function was used to construct support vector machines to classify the spoken word utterances. The support vector machines were constructed using various kernel functio...

This paper describes a classical algorithm carrying out dynamic 3D obstacle recognition for autonomous underwater vehicles (AUVs), Support Vector Machines (SVMs). SVM is an efficient algorithm that was developed for recognizing 3D object in recent years. A recognition system is designed using Support Vector Machines for applying the capabilities on appearance-based 3D obstacle recognition. All ...

Support Vector Machines (SVMs) have been recently proposed as a new technique for pattern recognition. In this paper, the SVMs with a binary tree recognition strategy are used to tackle the face recognition problem. We illustrate the potential of SVMs on the Cambridge ORL face database, which consists of 400 images of 40 individuals, containing quite a high degree of variability in expression, ...