Bayesian learning of probabilistic language models
Bayesian learning of probabilistic language models
An open graph visualization system and its applications to software engineering
Software—Practice & Experience - Special issue on discrete algorithm engineering
A new discriminative kernel from probabilistic models
Neural Computation
Using the Fisher Kernel Method to Detect Remote Protein Homologies
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
New Methods for Splice Site Recognition
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Optimization of HMM by a Genetic Algorithm
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 3 - Volume 3
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Evolving kernels for support vector machine classification
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A survey of evolutionary algorithms for clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Evolutionary computation in bioinformatics: a review
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Evolving the structure of hidden Markov models
IEEE Transactions on Evolutionary Computation
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Fisher kernels have been successfully applied to many problems in bioinformatics. However, their success depends on the quality of the generative model upon which they are built. For Fisher kernel techniques to be used on novel problems, a mechanism for creating accurate generative models is required. A novel framework is presented for automatically creating domain-specific generative models that can be used to produce Fisher kernels for support vector machines SVMs and other kernel methods. The framework enables the capture of prior knowledge and addresses the issue of domain-specific kernels, both of which are current areas that are lacking in many kernel-based methods. To obtain the generative model, genetic algorithms are used to evolve the structure of hidden Markov models HMMs. A Fisher kernel is subsequently created from the HMM, and used in conjunction with an SVM, to improve the discriminative power. This paper investigates the effectiveness of the proposed method, named GA-SVM. We show that its performance is comparable if not better than other state of the art methods in classifying secretory protein sequences of malaria. More interestingly, it showed better results than the sequence-similarity-based approach, without the need for additional homologous sequence information in protein enzyme family classification. The experiments clearly demonstrate that the GA-SVM is a novel way to find features with good performance from biological sequences, that does not require extensive tuning of a complex model.