Proper treatment of selections is essential in parametric feature-based design. Data exchange is one of the most important operators in any design paradigm. In this paper we addre...
Feature subset selection, applied as a pre-processing step to machine learning, is valuable in dimensionality reduction, eliminating irrelevant data and improving classifier perfo...
Algorithms for feature selection fall into two broad categories: wrappers that use the learning algorithm itself to evaluate the usefulness of features and filters that evaluate f...
Latent Semantic Indexing (LSI) has been validated to be effective on many small scale text collections. However, little evidence has shown its effectiveness on unsampled large sca...
This paper presents a method for selection of SIFT(Scale-Invariant Feature Transform) feature points using OC-SVM (One Class-Support Vector Machines). We proposed the method for au...