18.465 Topics in Statistics: Statistical Learning Theory, Spring 2004
Name
18-465Spring-2004.zip
Size
2.14 MB
Format
ZIP
Checksum (MD5)
a762e4acadb3fda65f9fa1e199923cf1
Author(s)
Panchenko, Dmitry A.
Alternative Title
Topics in Statistics: Statistical Learning Theory
Date Issued
June 2004
Abstract
The main goal of this course is to study the generalization ability of a number of popular machine learning algorithms such as boosting, support vector machines and neural networks. Topics include Vapnik-Chervonenkis theory, concentration inequalities in product spaces, and other elements of empirical process theory.
Subjects
machine learning algorithms
boosting
support vector machines
neural networks
Vapnik- Chervonenkis theory
concentration inequalities in product spaces
empirical process theory
MIT Department
Massachusetts Institute of Technology. Department of Mathematics
Terms of Use
Persistent DSpace Link
