Difference between revisions of "BIO Machine 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. | |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. | ||
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Revision as of 22:16, 14 January 2014
Machine Learning
This page is a placeholder, or under current development; it is here principally to establish the logical framework of the site. The material on this page is correct, but incomplete.
Overview of "classical" and current approaches to machine learning.
Contents
Introductory reading
Introduction
Paradigms
Neural Networks
Hidden Markov Models
Support Vector Machines
Bayesian Networks
Training sets
Gold standards as true positives and the problem of generating true negatives from non-observed data...
ROC and associated metrics
Receiver operating characteristic
Machine learning in R
Expand for R-script
Joshua Reich has posted a self-contained R-script for a number of machine learning topics. See below.
Further reading and resources
Hassanien et al. (2013) Computational intelligence techniques in bioinformatics. Comput Biol Chem 47:37-47. (pmid: 23891719) |
Salakhutdinov & Hinton (2012) An efficient learning procedure for deep Boltzmann machines. Neural Comput 24:1967-2006. (pmid: 22509963) |
Hinton et al. (2006) A fast learning algorithm for deep belief nets. Neural Comput 18:1527-54. (pmid: 16764513) |
Lodhi, H. (2012) Computational biology perspective: kernel methods and deep learning. WIREs: Computational Statistics 4(5):455-465. |
(pmid: None) [ Source URL ][ DOI ] Abstract |