Description: Kernel Based Algorithms for Mining Huge Data Sets by Te-Ming Huang, Vojislav Kecman, Ivica Kopriva Estimated delivery 3-12 business days Format Hardcover Condition Brand New Description "Kernel Based Algorithms for Mining Huge Data Sets" is the first book treating the fields of supervised, semi-supervised and unsupervised machine learning collectively. Publisher Description "Kernel Based Algorithms for Mining Huge Data Sets" is the first book treating the fields of supervised, semi-supervised and unsupervised machine learning collectively. The book presents both the theory and the algorithms for mining huge data sets by using support vector machines (SVMs) in an iterative way. It demonstrates how kernel based SVMs can be used for dimensionality reduction (feature elimination) and shows the similarities and differences between the two most popular unsupervised techniques, the principal component analysis (PCA) and the independent component analysis (ICA). The book presents various examples, software, algorithmic solutions enabling the reader to develop their own codes for solving the problems. The book is accompanied by a website for downloading both data and software for huge data sets modeling in a supervised and semisupervised manner, as well as MATLAB based PCA and ICA routines for unsupervised learning. The book focuses on a broad range of machine learning algorithms and it is particularly aimed at students, scientists, and practicing researchers in bioinformatics (gene microarrays), text-categorization, numerals recognition, as well as in the images and audio signals de-mixing (blind source separation) areas. Details ISBN 3540316817 ISBN-13 9783540316817 Title Kernel Based Algorithms for Mining Huge Data Sets Author Te-Ming Huang, Vojislav Kecman, Ivica Kopriva Format Hardcover Year 2006 Pages 260 Edition 2006th Publisher Springer-Verlag Berlin and Heidelberg GmbH & Co. KG GE_Item_ID:137566465; 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: 127.1 USD
Location: Fairfield, Ohio
End Time: 2025-01-02T03:42:49.000Z
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ISBN-13: 9783540316817
Book Title: Kernel Based Algorithms for Mining Huge Data Sets
Number of Pages: Xvi, 260 Pages
Publication Name: Kernel Based Algorithms for Mining Huge Data Sets : Supervised, Semi-Supervised, and Unsupervised Learning
Language: English
Publisher: Springer Berlin / Heidelberg
Subject: Engineering (General), Probability & Statistics / General, Algebra / General, Databases / Data Mining
Publication Year: 2006
Type: Textbook
Item Weight: 44.8 Oz
Item Length: 9.3 in
Author: Vojislav Kecman, Te-Ming Huang, Ivica Kopriva
Subject Area: Mathematics, Computers, Technology & Engineering
Item Width: 6.1 in
Series: Studies in Computational Intelligence Ser.
Format: Hardcover