Surrogate maximization (or minimization) (SM) algorithms are a family of algorithms that can be regarded as a generalization of expectation-maximization (EM) algorithms. There are...
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...
Convex programming involves a convex set F Rn and a convex cost function c : F R. The goal of convex programming is to find a point in F which minimizes c. In online convex prog...
Production scheduling, the problem of sequentially con guring a factory to meet forecasted demands, is a critical problem throughout the manufacturing industry. The requirement of...
Jeff G. Schneider, Justin A. Boyan, Andrew W. Moor...
Abstract: Our main contribution is to propose a novel model selection methodology, expectation minimization of information criterion (EMIC). EMIC makes a significant impact on the...