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...
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 ...
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...
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...
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 ...