Floating search methods in feature selection
Pattern Recognition Letters
APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29
An enhanced subspace method for face recognition
Pattern Recognition Letters
Pattern Recognition Letters
Sequence-driven features for prediction of subcellular localization of proteins
Pattern Recognition
Letters: Fusion of classifiers for protein fold recognition
Neurocomputing
Combination of hyperbolic functions for multimodal biometrics data fusion
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Deviance from perfection is a better criterion than closeness to evil when identifying risky code
Proceedings of the IEEE/ACM international conference on Automated software engineering
Applying VSM and LCS to develop an integrated text retrieval mechanism
Expert Systems with Applications: An International Journal
What you like in design use to correct bad-smells
Software Quality Control
Hi-index | 12.05 |
In this paper, we propose a new encoding technique that combines the different physicochemical properties of amino acids together with Needleman-Wunsch algorithm. The algorithm was tested in the recognition of T-cell epitopes. A series of SVM classifiers, where each SVM is trained using a different physicochemical property, combined with the ''max rule'' enables us to obtain an improvement over the state-of-the-art approaches.