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APIN
2002
121views more  APIN 2002»
14 years 9 months ago
Applying Learning by Examples for Digital Design Automation
This paper describes a new learning by example mechanism and its application for digital circuit design automation. This mechanism uses finite state machines to represent the infer...
Ben Choi
SDM
2010
SIAM
144views Data Mining» more  SDM 2010»
14 years 11 months ago
A Probabilistic Framework to Learn from Multiple Annotators with Time-Varying Accuracy
This paper addresses the challenging problem of learning from multiple annotators whose labeling accuracy (reliability) differs and varies over time. We propose a framework based ...
Pinar Donmez, Jaime G. Carbonell, Jeff Schneider
GECCO
2008
Springer
121views Optimization» more  GECCO 2008»
14 years 11 months ago
Fast rule representation for continuous attributes in genetics-based machine learning
Genetic-Based Machine Learning Systems (GBML) are comparable in accuracy with other learning methods. However, efficiency is a significant drawback. This paper presents a new rep...
Jaume Bacardit, Natalio Krasnogor
ML
2000
ACM
154views Machine Learning» more  ML 2000»
14 years 9 months ago
Lazy Learning of Bayesian Rules
The naive Bayesian classifier provides a simple and effective approach to classifier learning, but its attribute independence assumption is often violated in the real world. A numb...
Zijian Zheng, Geoffrey I. Webb
ICML
2010
IEEE
14 years 7 months ago
Constructing States for Reinforcement Learning
POMDPs are the models of choice for reinforcement learning (RL) tasks where the environment cannot be observed directly. In many applications we need to learn the POMDP structure ...
M. M. Hassan Mahmud