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Semi-Supervised Learning$
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Olivier Chapelle, Bernhard Scholkopf, and Alexander Zien

Print publication date: 2006

Print ISBN-13: 9780262033589

Published to MIT Press Scholarship Online: August 2013

DOI: 10.7551/mitpress/9780262033589.001.0001

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Prediction of Protein Function from Networks

Prediction of Protein Function from Networks

(p.361) 20 Prediction of Protein Function from Networks
Semi-Supervised Learning

Shin Hyunjung

Tsuda Koji

The MIT Press

This chapter describes an algorithm to assign weights to multiple graphs within graph-based semi-supervised learning. Both predicting class labels and searching for weights for combining multiple graphs are formulated into one convex optimization problem. The graph-combining method is applied to functional class prediction of yeast proteins. When compared with individual graphs, the combined graph with optimized weights performs significantly better than any single graph. When compared with the semi-definite programming-based support vector machine (SDP/SVM), it shows comparable accuracy in a remarkably short time. Compared with a combined graph with equal-valued weights, this method could select important graphs without loss of accuracy, which implies the desirable property of integration with selectivity.

Keywords:   graph-based semi-supervised learning, predicting class labels, searching for weights, convex optimization problem, graph-combining method, yeast proteins, semi-definite programming, SDP/SVM, integration with selectivity

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