Iterative learning algorithms that approximate the solution of support vector machines (SVMs) have two potential advantages. First, they allow for online and active learning. Seco...
We develop, analyze, and test a training algorithm for support vector machine classifiers without offset. Key features of this algorithm are a new, statistically motivated stoppi...
Evolutionary Algorithms’ (EAs’) application to real world optimization problems often involves expensive fitness function evaluation. Naturally this has a crippling effect on ...
We present a fast iterative support vector training algorithm for a large variety of different formulations. It works by incrementally changing a candidate support vector set usin...
S. V. N. Vishwanathan, Alex J. Smola, M. Narasimha...
— As Internet worms become ever faster and more sophisticated, it is important to be able to extract worm signatures in an accurate and timely manner. In this paper, we apply mac...
Stewart M. Yang, Jianping Song, Harish Rajamani, T...