Learning in Neural Networks: Theoretical Foundations
Learning in Neural Networks: Theoretical Foundations
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
A note on Platt's probabilistic outputs for support vector machines
Machine Learning
Neural Computation
VC Theory of Large Margin Multi-Category Classifiers
The Journal of Machine Learning Research
MSVMpack: A Multi-Class Support Vector Machine Package
The Journal of Machine Learning Research
A generic model of multi-class support vector machine
International Journal of Intelligent Information and Database Systems
Cascading discriminant and generative models for protein secondary structure prediction
PRIB'12 Proceedings of the 7th IAPR international conference on Pattern Recognition in Bioinformatics
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Support vector machines, let them be bi-class or multi-class, have proved efficient for protein secondary structure prediction. They can be used either as sequence-to-structure classifier, structure-to-structure classifier, or both. Compared to the classifier most commonly found in the main prediction methods, the multi-layer perceptron, they exhibit one single drawback: their outputs are not class posterior probability estimates. This paper addresses the problem of post-processing the outputs of multi-class support vector machines used as sequence-to-structure classifiers with a structure-to-structure classifier estimating the class posterior probabilities. The aim of this comparative study is to obtain improved performance with respect to both criteria: prediction accuracy and quality of the estimates.