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UAI
2004
15 years 3 months ago
The Minimum Information Principle for Discriminative Learning
Exponential models of distributions are widely used in machine learning for classification and modelling. It is well known that they can be interpreted as maximum entropy models u...
Amir Globerson, Naftali Tishby
120
Voted
GPEM
2008
128views more  GPEM 2008»
15 years 1 months ago
Coevolutionary bid-based genetic programming for problem decomposition in classification
In this work a cooperative, bid-based, model for problem decomposition is proposed with application to discrete action domains such as classification. This represents a significan...
Peter Lichodzijewski, Malcolm I. Heywood
107
Voted
MM
2004
ACM
124views Multimedia» more  MM 2004»
15 years 7 months ago
An online-optimized incremental learning framework for video semantic classification
This paper considers the problems of feature variation and concept uncertainty in typical learning-based video semantic classification schemes. We proposed a new online semantic c...
Jun Wu, Xian-Sheng Hua, HongJiang Zhang, Bo Zhang
123
Voted
AAMAS
2008
Springer
15 years 1 months ago
Agents that argue and explain classifications
Argumentation is a promising approach used by autonomous agents for reasoning about inconsistent/incomplete/uncertain knowledge, based on the construction and the comparison of ar...
Leila Amgoud, Mathieu Serrurier
114
Voted
ICML
2009
IEEE
16 years 2 months ago
ABC-boost: adaptive base class boost for multi-class classification
We propose abc-boost (adaptive base class boost) for multi-class classification and present abc-mart, an implementation of abcboost, based on the multinomial logit model. The key ...
Ping Li