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- Author:
-
Panchenko, Dmitry
- Subject:
- Mathematics and Statistics
- Institution Name:
- M.I.T.
- Collection:
-
MIT OpenCourseWare
- Grade Level:
- Post-secondary
- 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.
- Languages:
- English
- Material Type:
- Full Course, Homework and Assignments, Lecture Notes, Syllabi
- Media Format:
- Text/HTML, Downloadable docs
- Conditions of Use:
-
Creative Commons Attribution-Noncommercial-Share Alike 3.0
No restrictions on your remixing, redistributing, or making derivative works.
Give credit to the author, as required.
Your remixing, redistributing, or making derivatives works comes with some
restrictions, including how it is shared.
Your redistributing comes with some restrictions. Do not remix or make
derivative works.
Copyrighted materials, available under Fair Use and the TEACH Act for US-based
educators, or other custom arrangements. Go to the resource provider to see
their individual restrictions.
Topics in Statistics: Statistical Learning Theory, Spring 2004
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