Description: About this Item The item is a book Hardback The Author Name is S.Y. Kung The Title is Kernel Methods and Machine Learning Condition New Other Comments Pages Count - 616. Binding type - Hardcover. Content Language - English Category - COMPUTERS / Artificial Intelligence / Computer Vision & Patt COMPUTERS / Artificial Intelligence / General Product Description - Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles. It provides over 30 major theorems for kernel-based supervised and unsupervised learning models. The first of the theorems establishes a condition, arguably necessary and sufficient, for the kernelization of learning models. In addition, several other theorems are devoted to proving mathematical equivalence between seemingly unrelated models. With over 25 closed-form and iterative algorithms, the book provides a step-by-step guide to algorithmic procedures and analysing which factors to consider in tackling a given problem, enabling readers to improve specifically designed learning algorithms, build models for new applications and develop efficient techniques suitable for green machine learning technologies. Numerous real-world examples and over 200 problems, several of which are Matlab-based simulation exercises, make this an essential resource for graduate students and professionals in computer science, electrical and biomedical engineering. Solutions to problems are provided online for instructors. We Use Stock Images Because we have over 2 million items for sale we have to use stock images, this listing does not include the actual image of the item for sale. The purchase of this specific item is made with the understanding that the image shown in this listing is a stock image and not the actual item for sale. For example: some of our stock images include stickers, labels, price tags, hyper stickers, obi's, promotional messages, signatures and or writing which may not be available in the actual item. When possible we will add details of the items we are selling to help buyers know what is included in the item for sale. The details are provided automatically from our central master database and can sometimes be wrong. Books are released in many editions and variations, such as standard edition, re-issue, not for sale, promotional, special edition, limited edition, and many other editions and versions. The Book you receive could be any of these editions or variations. If you are looking for a specific edition or version please contact us to verify what we are selling. Gift IdeasThis is a great Christmas gift idea. Hours of ServiceWe have many warehouses, some of the warehouses process orders seven days a week, but the Administration Support Staff are located at a head office location, outside of the warehouses, and typically work only Monday to Friday. Location ID 9000z iHaveit SKU ID 165969615
Price: 124.22 USD
Location: US
End Time: 2024-11-10T17:49:39.000Z
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Restocking Fee: No
Return shipping will be paid by: Buyer
All returns accepted: Returns Accepted
Item must be returned within: 30 Days
Refund will be given as: Money Back
Fiction/Non-Fiction: Non-Fiction
Genre/Subject: COMPUTERS / Artificial Intelligence / Computer Vision Patt
Brand: Cambridge University Press
Weight: 1.18
Style: NA
Title: Kernel Methods and Machine Learning
Release Title: Kernel Methods and Machine Learning
Record Grading: New
Sleeve Grading: New
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Size: NA
Film/TV Title: Kernel Methods and Machine Learning
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Movie/TV Title: Kernel Methods and Machine Learning
UPC: 9781107024960
EAN: 9781107024960
ISBN: 9781107024960
Binding Type: Hardcover
Main Stone: NA
Metal Purity: NA
Metal: NA
Connectivity: NA
Model: NA
Number of Pages: 572 Pages
Publication Name: Kernel Methods and Machine Learning
Language: English
Publisher: Cambridge University Press
Publication Year: 2014
Item Height: 1.1 in
Subject: Computer Vision & Pattern Recognition
Type: Textbook
Item Weight: 47.6 Oz
Item Length: 9.9 in
Author: S. Y. Kung
Subject Area: Computers
Item Width: 6.9 in
Format: Hardcover
