Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
471182 | Computers & Mathematics with Applications | 2008 | 10 Pages |
Abstract
We present the results of an information theory-based approach to select an optimal subset of features for the prediction of protein model quality. The optimal subset of features was calculated by means of a backward selection procedure. The performances of a probabilistic classifier modeled by means of a Kernel Probability Density Estimation method (KPDE) were compared with those of a feed-forward Artificial Neural Network (ANN) and a Support Vector Machine (SVM).
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Alfonso Montuori, Giovanni Raimondo, Eros Pasero,