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- W1942457562 abstract "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." @default.
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- W1942457562 date "2011-01-01" @default.
- W1942457562 modified "2023-09-29" @default.
- W1942457562 title "Estimating the Class Posterior Probabilities in Protein Secondary Structure Prediction" @default.
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- W1942457562 doi "https://doi.org/10.1007/978-3-642-24855-9_23" @default.
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