CHAPTER 4
Support Vector Regression
The key to artificial intelligence has always been the representation.
—Jeff Hawkins
Rooted in statistical learning or Vapnik-Chervonenkis (VC) theory, support vector machines (SVMs) are well positioned to generalize on yet-to-be-seen data. The SVM concepts presented in Chapter 3 can be generalized to become applicable to regression problems. As in classification, support vector regression (SVR) is characterized by the use of kernels, sparse solution, and VC control of the margin and the number of support vectors. Although less popular than SVM, SVR has been proven to be an effective tool in real-value function ...
Get Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
