The existing methods for offline training of cascade classifiers take a greedy search to optimize individual classifiers in the cascade, leading inefficient overall performance. W...
Despite the fact that many symbolic and connectionist (neural net) learning algorithms are addressing the same problem of learning from classified examples, very little Is known r...
Raymond J. Mooney, Jude W. Shavlik, Geoffrey G. To...
This paper addresses the issue of the explanation of the result given to the end-user by a classifier, when it is used as a decision support system. We consider machine learning cl...
- This paper presents a supervised learning based power management framework for a multi-processor system, where a power manager (PM) learns to predict the system performance state...
Consider a supervised learning problem in which examples contain both numerical- and text-valued features. To use traditional featurevector-based learning methods, one could treat...
Sofus A. Macskassy, Haym Hirsh, Arunava Banerjee, ...