Abstract. Input feature ranking and selection represent a necessary preprocessing stage in classification, especially when one is required to.

Input feature ranking and selection represent a necessary preprocessing stage in classification, especially when one is required to manage large quantities ...

Input feature ranking and selection represent a necessary preprocessing stage in classification, especially when one is required to manage large quantities ...

We introduce a weighted LVQ algorithm, called Energy Relevance LVQ (ERLVQ), based on Onicescu's informational energy [10]. ERLVQ is an incremental learning ...

We describe a kernel method which uses the maximization of Onicescu's informational energy as a criteria for computing the relevances of input features.

A kernel method which uses the maximization of Onicescu's informational energy as a criteria for computing the relevances of input features and obtains an ...

The paper deals with the concept of relevance learning in learning vector quantization and classification. Recent machine learning approaches with the ability ...

Feature ranking fusion for text classifier. Feature ranking is widely used in text classification. · Energy Supervised Relevance Neural Gas for Feature Ranking.

Abstract—Input feature ranking and selection represent a necessary preprocessing stage in classification, especially when.

Jul 10, 2010 · Andonie R, Caţaron A (2004) An informational energy LVQ approach for feature ranking. ... feature ranking methods based on information entropy.