Feature selection has proven to be a valuable technique in supervised learning for improving predictive accuracy while reducing the number of attributes considered in a task. We i...
The paper presents a novel method for compressing large database workloads for purpose of autonomic, continuous index selection. The compressed workload contains a small subset of ...
This paper proposes a novel lexicalized approach for rule selection for syntax-based statistical machine translation (SMT). We build maximum entropy (MaxEnt) models which combine ...
Mutual information may be used to select the embedding lag of a time series. However, this lag selection is usually limited to the analysis of the mutual information between a pair...
A novel approach to combining clustering and feature selection is presented. It implements a wrapper strategy for feature selection, in the sense that the features are directly se...