Multiple Kernel Learning for Fold Recognition

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Multiple Kernel Learning for Fold Recognition
Fold recognition is a key problem in computational biology that involves classifying protein sharing structural similarities into classes commonly known as "folds". Recently, researchers have developed several efficient kernel based discriminatory methods for fold classification using sequence information. These methods train one-versus-rest binary classifiers using well optimized kernels from different data sources and techniques. Integrating this vast amount of data in the form of kernel matrices is an interesting and challenging problem. The semidefinite positive property of the various kernel matrices makes it attractive to cast the task of learning an optimal weighting of several kernel matrices as a semi-definite programming optimization problem. We experiment with a previously introduced quadratically constrained quadratic optimization problem for kernel integration using 1-norm and 2-norm support vector machines. We integrate state-of-the-art profilebased direct kern...
Huzefa Rangwala
Added 10 Feb 2011
Updated 10 Feb 2011
Type Journal
Year 2010
Authors Huzefa Rangwala
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