EbookBell.com

Most ebook files are in PDF format, so you can easily read them using various software such as Foxit Reader or directly on the Google Chrome browser.
Some ebook files are released by publishers in other formats such as .awz, .mobi, .epub, .fb2, etc. You may need to install specific software to read these formats on mobile/PC, such as Calibre.

Please read the tutorial at this link: https://ebookbell.com/faq

We offer FREE conversion to the popular formats you request; however, this may take some time. Therefore, right after payment, please email us, and we will try to provide the service as quickly as possible.

For some exceptional file formats or broken links (if any), please refrain from opening any disputes. Instead, email us first, and we will try to assist within a maximum of 6 hours.

EbookBell Team

Support Vector Machines Optimization Based Theory Algorithms And Extensions Naiyang Deng Yingjie Tian Chunhua Zhang

Name: Support Vector Machines Optimization Based Theory Algorithms And Extensions Naiyang Deng Yingjie Tian Chunhua Zhang
SKU: BELL-4394072
Price: 31.00 USD
Availability: InStock
Rating: 4.8 (44 reviews)

SKU: BELL-4394072

$ 31.00 ~~$ 45.00~~ (-31%)

4.8

44 reviews

Support Vector Machines Optimization Based Theory Algorithms And Extensions Naiyang Deng Yingjie Tian Chunhua Zhang instant download after payment.

Publisher: CRC Press Taylor & Francis Group

File Extension: PDF

File size: 2.33 MB

Pages: 345

Author: Naiyang Deng; Yingjie Tian; Chunhua Zhang

ISBN: 9781439857922, 143985792X

Language: English

Year: 2012

Product desciption

Support Vector Machines Optimization Based Theory Algorithms And Extensions Naiyang Deng Yingjie Tian Chunhua Zhang by Naiyang Deng; Yingjie Tian; Chunhua Zhang 9781439857922, 143985792X instant download after payment.

''Preface Support vector machines (SVMs), which were introduced by Vapnik in the early 1990s, are proved effective and promising techniques for data mining. SVMs have recently been breakthroughs in advance in their theoretical studies and implementations of algorithms. They have been successfully applied in many fields such as text categorization, speech recognition, remote sensing image analysis, time series forecasting, information security and etc. SVMs, having their roots in Statistical Learning Theory (SLT) and optimization methods, become powerful tools to solve the problems of machine learning with finite training points and to overcome some traditional difficulties such as the ''curse of dimensionality'', ''over-fitting'' and etc. SVMs theoretical foundation and implementation techniques have been established and SVMs are gaining quick development and popularity due to their many attractive features: nice mathematical representations, geometrical explanations, good generalization abilities and promising empirical performance. Some SVM monographs, including more sophisticated ones such as Cristianini & Shawe-Taylor [39] and Scholkopf & Smola [124], have been published. We have published two books about SVMs in Science Press of China since 2004 [42, 43], which attracted widespread concerns and received favorable comments. After several years research and teaching, we decide to rewrite the books and add new research achievements. The starting point and focus of the book is optimization theory, which is different from other books on SVMs in this respect. Optimization is one of the pillars on which SVMs are built, so it makes a lot of sense to consider them from this point of view''--