This thesis identiļ¬es and extends techniques that can be linked to the principle
of maximum entropy (maxent) and applied to parameter estimation in machine
learning and statistics. Entropy functions based on deformed logarithms are used
to construct Bregman divergences, and together these represent a generalization
of relative entropy. The framework is analyzed using convex analysis to charac-
terize generalized forms of exponential family distributions. Various connections
to the existing machine learning literature are discussed and the techniques are
applied to the problem of non-negative matrix factorization (NMF).
| Identifer | oai:union.ndltd.org:ADTP/233142 |
| Date | January 2008 |
| Creators | Sears, Timothy Dean, tim.sears@biogreenoil.com |
| Publisher | The Australian National University. Research School of Information Sciences and Engineering |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | http://www.anu.edu.au/legal/copyrit.html), Copyright Timothy Dean Sears |
Page generated in 0.0016 seconds