Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
The nature of statistical learning theory
The nature of statistical learning theory
Are Multilayer Perceptrons Adequate for Pattern Recognition and Verification?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Protein Folding Class Predictor for SCOP: Approach Based on Global Descriptors
Proceedings of the 5th International Conference on Intelligent Systems for Molecular Biology
A hierarchical method for multi-class support vector machines
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Hybrid generative/discriminative classifier for unconstrained character recognition
Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
Ensemble classifier for protein fold pattern recognition
Bioinformatics
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Some new features for protein fold prediction
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Binary tree of SVM: a new fast multiclass training and classification algorithm
IEEE Transactions on Neural Networks
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
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Predicting the three-dimensional (3D) structure of a protein is a key problem in molecular biology It is also an interesting issue for statistical methods recognition There are many approaches to this problem considering discriminative and generative classifiers In this paper a classifier combining the well-known Support Vector Machine (SVM) classifier with Regularized Discriminant Analysis (RDA) classifier is presented It is used on a real world data set The obtained results improve previously published methods.