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...
Recommender systems perform much better on users for which they have more information. This gives rise to a problem of satisfying users new to a system. The problem is even more a...
Hidden Markov Models (HMMs) are an useful and widely utilized approach to the modeling of data sequences. One of the problems related to this technique is finding the optimal stru...
In this paper, we propose an extended local search framework to solve combinatorial optimization problems with data uncertainty. Our approach represents a major departure from sce...
The problem of dividing a sequence of values into segments occurs in database systems, information retrieval, and knowledge management. The challenge is to select a finite number ...