| 查看: 192 | 回复: 0 | |||
| 当前主题已经存档。 | |||
[资源]
Learning with Kernels: Support Vector Machines
|
|||
|
Learning with Kernels: Support Vector Machines Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning) by Bernhard Schlkopf, Alexander J. Smola Publisher: The MIT Press; 1st edition (December 15, 2001) | ISBN-10: 0262194759 | PDF | 36,2 Mb | 644 pages In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs— -kernels--for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics. Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years. [ Last edited by fenghbu on 2007-3-29 at 16:04 ] |
回复此楼» 猜你喜欢
存款400万可以在学校里躺平吗
已经有7人回复
-
基金委咋了？2026年的指南还没有出来？
已经有10人回复
-
拟解决的关键科学问题还要不要写
已经有6人回复
-
基金申报
已经有6人回复
-
推荐一本书
已经有13人回复
-
国自然申请面上模板最新2026版出了吗？
已经有17人回复
-
纳米粒子粒径的测量
已经有8人回复
-
疑惑？
已经有5人回复
-
计算机、0854电子信息（085401-058412）调剂
已经有5人回复
-
Materials Today Chemistry审稿周期
已经有5人回复