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Computational biology perspective: kernel methods and deep learning

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Abstract The field of machine learning provides useful means and tools for finding accurate solutions to complex and challenging biological problems. In recent years a class of learning algorithms namely kernel methods has been successfully applied to various tasks in computational biology. In this article we present an overview of kernel methods and support vector machines and focus on their applications to biological sequences. We also describe a new class of approaches that is termed as deep learning. These techniques have desirable characteristics and their use can be highly effective within the field of computational biology. WIREs Comput Stat 2012 doi: 10.1002/wics.1223 This article is categorized under: Applications of Computational Statistics > Computational and Molecular Biology Statistical Learning and Exploratory Methods of the Data Sciences > Neural Networks Statistical Learning and Exploratory Methods of the Data Sciences > Support Vector Machines

A maximal margin hyperplane.

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Hard margin in noisy environments.

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Hard margin in noise free environments.

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Statistical Learning and Exploratory Methods of the Data Sciences > Support Vector Machines
Statistical Learning and Exploratory Methods of the Data Sciences > Neural Networks
Applications of Computational Statistics > Computational and Molecular Biology

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