In this paper we describe a method to learn parameters
which govern pedestrian motion by observing video
data. Our learning framework is based on variational
mode learning and a...
Recent years have witnessed a growing interest in analogical learning for NLP applications. If the principle of analogical learning is quite simple, it does involve complex steps ...
Boosting algorithms build highly accurate prediction mechanisms from a collection of lowaccuracy predictors. To do so, they employ the notion of weak-learnability. The starting po...
Semi-naive Bayesian classifiers seek to retain the numerous strengths of naive Bayes while reducing error by weakening the attribute independence assumption. Backwards Sequential ...
Abstract. In this paper we present a novel approach to solving Constraint Satisfaction Problems whose constraint graphs are highly clustered and the graph of clusters is close to b...