Description: Kernel-based Data Fusion for Machine Learning by Shi Yu, Bart Moor, Yves Moreau, Léon-Charles Tranchevent Estimated delivery 3-12 business days Format Hardcover Condition Brand New Description This book provides a specific introduction to data fusion problems using support vector machines. In the first part, this book begins with a brief survey of additive models and Rayleigh quotient objectives in machine learning, and then introduces kernel fusion as the additive expansion of support vector machines in the dual problem. Publisher Description Data fusion problems arise frequently in many different fields. This book provides a specific introduction to data fusion problems using support vector machines. In the first part, this book begins with a brief survey of additive models and Rayleigh quotient objectives in machine learning, and then introduces kernel fusion as the additive expansion of support vector machines in the dual problem. The second part presents several novel kernel fusion algorithms and some real applications in supervised and unsupervised learning. The last part of the book substantiates the value of the proposed theories and algorithms in MerKator, an open software to identify disease relevant genes based on the integration of heterogeneous genomic data sources in multiple species. The topics presented in this book are meant for researchers or students who use support vector machines. Several topics addressed in the book may also be interesting to computational biologists who want to tackle data fusion challenges in real applications. The background required of the reader is a good knowledge of data mining, machine learning and linear algebra. Details ISBN 3642194052 ISBN-13 9783642194054 Title Kernel-based Data Fusion for Machine Learning Author Shi Yu, Bart Moor, Yves Moreau, Léon-Charles Tranchevent Format Hardcover Year 2011 Pages 214 Edition 11001st Publisher Springer-Verlag Berlin and Heidelberg GmbH & Co. KG GE_Item_ID:140236952; About Us Grand Eagle Retail is the ideal place for all your shopping needs! With fast shipping, low prices, friendly service and over 1,000,000 in stock items - you're bound to find what you want, at a price you'll love! Shipping & Delivery Times Shipping is FREE to any address in USA. Please view eBay estimated delivery times at the top of the listing. Deliveries are made by either USPS or Courier. We are unable to deliver faster than stated. International deliveries will take 1-6 weeks. NOTE: We are unable to offer combined shipping for multiple items purchased. This is because our items are shipped from different locations. Returns If you wish to return an item, please consult our Returns Policy as below: Please contact Customer Services and request "Return Authorisation" before you send your item back to us. Unauthorised returns will not be accepted. Returns must be postmarked within 4 business days of authorisation and must be in resellable condition. Returns are shipped at the customer's risk. We cannot take responsibility for items which are lost or damaged in transit. For purchases where a shipping charge was paid, there will be no refund of the original shipping charge. Additional Questions If you have any questions please feel free to Contact Us. Categories Baby Books Electronics Fashion Games Health & Beauty Home, Garden & Pets Movies Music Sports & Outdoors Toys
Price: 218.07 USD
Location: Fairfield, Ohio
End Time: 2024-11-24T03:44:56.000Z
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Restocking Fee: No
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ISBN-13: 9783642194054
Book Title: Kernel-based Data Fusion for Machine Learning
Number of Pages: Xiv, 214 Pages
Language: English
Publication Name: Kernel-Based Data Fusion for Machine Learning : Methods and Applications in Bioinformatics and Text Mining
Publisher: Springer Berlin / Heidelberg
Subject: Engineering (General), Intelligence (Ai) & Semantics, Databases / Data Mining, Life Sciences / Biology
Publication Year: 2011
Item Weight: 19.5 Oz
Type: Textbook
Author: Bart Moor, Shi Yu, Léon-Charles Tranchevent, Yves Moreau
Item Length: 9.3 in
Subject Area: Computers, Technology & Engineering, Science
Item Width: 6.1 in
Series: Studies in Computational Intelligence Ser.
Format: Hardcover