Kernel-based Data Fusion for Machine Learning: Methods and by Shi Yu, Léon-Charles Tranchevent, Bart Moor, Yves Moreau

By Shi Yu, Léon-Charles Tranchevent, Bart Moor, Yves Moreau

Data fusion difficulties come up usually in lots of varied fields. This e-book presents a particular advent to info fusion difficulties utilizing help vector machines. within the first half, this ebook starts off with a quick survey of additive versions and Rayleigh quotient targets in laptop studying, after which introduces kernel fusion because the additive enlargement of help vector machines within the twin challenge. the second one half offers a number of novel kernel fusion algorithms and a few actual purposes in supervised and unsupervised studying. The final a part of the ebook substantiates the price of the proposed theories and algorithms in MerKator, an open software program to spot ailment suitable genes in response to the combination of heterogeneous genomic info assets in a number of species.

The issues offered during this publication are intended for researchers or scholars who use aid vector machines. a number of subject matters addressed within the e-book can also be fascinating to computational biologists who are looking to take on facts fusion demanding situations in actual purposes. The heritage required of the reader is an efficient wisdom of knowledge mining, computer studying and linear algebra.

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Extra resources for Kernel-based Data Fusion for Machine Learning: Methods and Applications in Bioinformatics and Text Mining

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Chapter 7 discusses Canonical Correlation Analysis, a different unsupervised learning problem than clustering. A new method called Weighted Multiple Kernel Canonical Correlation Analysis (WMKCCA) is proposed to leverage the importance of different data sources in the CCA objective [86]. Beside the derivation of mathematical models, we present some preliminary results of using the mappings obtained by WMKCCA as the common information extracted from multiple data sources. Chapter 8 continues to discuss the gene prioritization problem started in Chapter 5.

Instead, α is solved via convex optimization. 3 Summary In this chapter we made a survey of several popular machine learning algorithms. The main finding was that these problems can all be simplified as the Generalized Rayleigh quotient form, given by minimize (W T PW )−1 (W T QW ) or maximize(W T PW )−1 (W T QW ). 1 we summarize the objectives, the mappings and the constraints corresponding of these algorithms in terms of Generalized Rayleigh quotient. 1 Summary of several machine algorithms and the mappings to the Generalized Rayleigh quotient form 36 2 Rayleigh Quotient-Type Problems in Machine Learning References 37 References 1.

Methods of Genomic Data Fusion: An Overview. Technical Report, Institute of Computer Science, University of Tartu (2006) 80. : The Nature of Statistical Learning Theory, 2nd edn. Springer, New York (1999) 26 1 Introduction 81. : Statistical Learning Theory. Wiley Interscience, New York (1998) 82. : On the uniform convergence of relative frequencies of events to their probabilities. Theory of Probability and its Applications 16, 264–280 (1971) 83. org/wiki/Antikythera_mechanism 84. : Multi-class Discriminant Kernel Learning via Convex Programming.

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