Given a classification problem, our goal is to find a low-dimensional linear transformation of the feature vectors which retains information needed to predict the class labels. We...
This paper presents a general and efficient framework for probabilistic inference and learning from arbitrary uncertain information. It exploits the calculation properties of fini...
Discriminative training for structured outputs has found increasing applications in areas such as natural language processing, bioinformatics, information retrieval, and computer ...
In order to understand AdaBoost’s dynamics, especially its ability to maximize margins, we derive an associated simplified nonlinear iterated map and analyze its behavior in lo...
Cynthia Rudin, Ingrid Daubechies, Robert E. Schapi...
This paper proposes the applications of soft computing to deal with the constraints in conventional modelling techniques of the dynamic extrusion process. The proposed technique i...
Leong Ping Tan, Ahmad Lotfi, Eugene Lai, J. B. Hul...