Lbr is a lazy semi-naive Bayesian classi er learning technique, designed to alleviate the attribute interdependence problem of naive Bayesian classi cation. To classify a test exa...
In this paper we present an application of explanation-based learning (EBL) in the parsing module of a real-time English-Spanish machine translation system designed to translate c...
Janine Toole, Fred Popowich, Devlan Nicholson, Dav...
This paper presents a new approach for designing test sequences to be generated on–chip. The proposed technique is based on machine learning, and provides a way to generate effi...
Christophe Fagot, Patrick Girard, Christian Landra...
We show how nonlinear embedding algorithms popular for use with shallow semisupervised learning techniques such as kernel methods can be applied to deep multilayer architectures, ...
We present an application of inductive concept learning and interactive visualization techniques to a large-scale commercial data mining project. This paper focuses on design and c...
William H. Hsu, Michael Welge, Thomas Redman, Davi...