Frame based methods for unconstrained optimization
Journal of Optimization Theory and Applications
Analysis of Generalized Pattern Searches
SIAM Journal on Optimization
Using the Fisher Kernel Method to Detect Remote Protein Homologies
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Text classification using string kernels
The Journal of Machine Learning Research
Convex Optimization
Mesh Adaptive Direct Search Algorithms for Constrained Optimization
SIAM Journal on Optimization
Optimal kernel selection in Kernel Fisher discriminant analysis
ICML '06 Proceedings of the 23rd international conference on Machine learning
Time-resolved spectrum kernel for biosonar target classification
SPPR'07 Proceedings of the Fourth conference on IASTED International Conference: Signal Processing, Pattern Recognition, and Applications
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In many applications, one is interested to detect certain patterns in random process signals. We consider a class of random process signals which contain sub-similarities at random positions representing the texture of an object. Those repetitive parts may occur in speech, musical pieces and sonar signals. We suggest a warped time-resolved spectrum kernel for extracting the subsequence similarity in time series in general, and as an example in biosonar signals. Having a set of those kernels for similarity extraction in different size of subsequences, we propose a new method to find an optimal linear combination of those kernels. We formulate the optimal kernel selection via maximizing the kernel Fisher discriminant (KFD) criterion and use Mesh Adaptive Direct Search (MADS) method to solve the optimization problem. Our method is used for biosonar landmark classification with promising results.