![[Search]](/static/images/search.gif){kind=small}
![[A-Z Index]](/static/images/az.gif){kind=small}
![[Contact]](/static/images/contact.gif){kind=small}
![[University of Cambridge]](/static/images/identifier2.gif){kind=small}
![[Talks.cam]](/static/images/talkslogosmall.gif)
Maria Lomeli
Thursday 15 February 2018, 13:30-15:00
Statistical Learning Theory
- đ¤ Speaker: Maria Lomeli
- đ Date & Time: Thursday 15 February 2018, 13:30 - 15:00
- đ Venue: Engineering Department, CBL Seminar Room 4-38
Abstract
Abstract
The wikipedia definition of Statistical Learning Theory says that it is a framework for machine learning drawing from the fields of statistics and functional analysis. It deals with the problem of finding a predictive function based on data. According to Vapnik (1990), “abstract learning theory established some conditions for generalization which are more general than those discussed in classical statistical paradigms and the understanding of these conditions inspired new algorithmic approaches to function estimation problems.” I will cover some introductory material, including the following topics:
Different loss functions Learning algorithms: Empirical risk minimisation and regularisation How can we solve each problem (first order methods only) VC dimension and some bounds
Recommended Reading
Introduction to Statistical Learning Theory, Bousquet, O., Boucheron, S. and Lugosi, G.
MIT Statistical Learning Theory and Applications course and RegML summer school notes by Lorenzo Rosasco
An Overview of Statistical Learning theory, Vapnik, V.
Series This talk is part of the Machine Learning Reading Group @ CUED series.
Included in Lists
- All Talks (aka the CURE list)
- bld31
- Cambridge Centre for Data-Driven Discovery (C2D3)
- Cambridge Forum of Science and Humanities
- Cambridge Language Sciences
- Cambridge talks
- Cambridge University Engineering Department Talks
- Centre for Smart Infrastructure & Construction
- Chris Davis' list
- Computational Continuum Mechanics Group Seminars
- custom
- Engineering Department, CBL Seminar Room 4-38
- Featured lists
- Guy Emerson's list
- Hanchen DaDaDash
- Inference Group Journal Clubs
- Inference Group Summary
- Information Engineering Division seminar list
- Interested Talks
- Machine Learning Reading Group
- Machine Learning Reading Group @ CUED
- Machine Learning Summary
- ML
- ndk22's list
- ob366-ai4er
- Quantum Matter Journal Club
- Required lists for MLG
- rp587
- School of Technology
- Simon Baker's List
- TQS Journal Clubs
- Trust & Technology Initiative - interesting events
- yk373's list
- yk449
Note: Ex-directory lists are not shown.