Abstract. In recent years, support vector machines (SVMs) have become a popular tool for pattern recognition and machine learning. Training a SVM involves solving a constrained qua...
In this paper, we investigate an approach based on support vector machines (SVMs) for detection of microcalcification (MC) clusters in digital mammograms, and propose a successive ...
Issam El-Naqa, Yongyi Yang, Miles N. Wernick, Niko...
Semi-supervised support vector machines (S3VMs) are a kind of popular approaches which try to improve learning performance by exploiting unlabeled data. Though S3VMs have been fou...
We present an improvement of Noviko 's perceptron convergence theorem. Reinterpreting this mistakebound as a margindependent sparsity guarantee allows us to give a PAC{style ...
Thore Graepel, Ralf Herbrich, Robert C. Williamson
Classifiers are traditionally learned using sets of positive and negative training examples. However, often a classifier is required, but for training only an incomplete set of pos...