Neural Computation
Learning in Neural Networks: Theoretical Foundations
Learning in Neural Networks: Theoretical Foundations
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Cascaded Bidirectional Recurrent Neural Networks for Protein Secondary Structure Prediction
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
VC Theory of Large Margin Multi-Category Classifiers
The Journal of Machine Learning Research
Modeling state durations in hidden Markov models for automatic speech recognition
ICASSP'92 Proceedings of the 1992 IEEE international conference on Acoustics, speech and signal processing - Volume 1
MSVMpack: A Multi-Class Support Vector Machine Package
The Journal of Machine Learning Research
Estimating the class posterior probabilities in protein secondary structure prediction
PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
A generic model of multi-class support vector machine
International Journal of Intelligent Information and Database Systems
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Most of the state-of-the-art methods for protein seconday structure prediction are complex combinations of discriminant models. They apply a local approach of the prediction which is known to induce a limit on the expected prediction accuracy. A priori, the use of generative models should make it possible to overcome this limitation. However, among the numerous hidden Markov models which have been dedicated to this task over more than two decades, none has come close to providing comparable performance. A major reason for this phenomenon is provided by the nature of the relevant information. Indeed, it is well known that irrespective of the model implemented, the prediction should benefit significantly from the availability of evolutionary information. Currently, this knowledge is embedded in position-specific scoring matrices which cannot be processed easily with hidden Markov models. With this observation at hand, the next significant advance should come from making the best of the two approaches, i.e., using a generative model on top of discriminant models. This article introduces the first hybrid architecture of this kind with state-of-the-art performance. The conjunction of the two levels of treatment makes it possible to optimize the recognition rate both at the residue level and at the segment level.