{
"item_title" : "Support Vector Machines",
"item_author" : [" Naiyang Deng", "Yingjie Tian", "Chunhua Zhang "],
"item_description" : "Enabling a sound understanding of SVMs, this book gives readers the tools to solve real-world problems using SVMs. It presents an accessible treatment of the two main components of SVMs-classification problems and regression problems. The authors emphasize the close connection between optimization theory and SVMs since optimization is one of the pillars on which SVMs are built. They construct SVMs for semi-supervised, knowledge-based, and robust classification problems. They also cover SVMs for Universum, privileged, multi-class, multi-instance, and multi-label classification problems.",
"item_img_path" : "https://covers3.booksamillion.com/covers/bam/1/43/985/792/143985792X_b.jpg",
"price_data" : {
"retail_price" : "166.99", "online_price" : "166.99", "our_price" : "166.99", "club_price" : "166.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : ""
}
}
Support Vector Machines : Optimization Based Theory, Algorithms, and Extensions
Overview
Enabling a sound understanding of SVMs, this book gives readers the tools to solve real-world problems using SVMs. It presents an accessible treatment of the two main components of SVMs-classification problems and regression problems. The authors emphasize the close connection between optimization theory and SVMs since optimization is one of the pillars on which SVMs are built. They construct SVMs for semi-supervised, knowledge-based, and robust classification problems. They also cover SVMs for Universum, privileged, multi-class, multi-instance, and multi-label classification problems.
This item is Non-Returnable
Customers Also Bought
Details
- ISBN-13: 9781439857922
- ISBN-10: 143985792X
- Publisher: CRC Press
- Publish Date: December 2012
- Dimensions: 9.4 x 6.1 x 0.9 inches
- Shipping Weight: 1.4 pounds
- Page Count: 364
Related Categories
