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» A Preference Model for Structured Supervised Learning Tasks
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JAIR
2008
148views more  JAIR 2008»
14 years 9 months ago
Learning Partially Observable Deterministic Action Models
We present exact algorithms for identifying deterministic-actions' effects and preconditions in dynamic partially observable domains. They apply when one does not know the ac...
Eyal Amir, Allen Chang
PR
2010
147views more  PR 2010»
14 years 8 months ago
Minimum classification error learning for sequential data in the wavelet domain
Wavelet analysis has found widespread use in signal processing and many classification tasks. Nevertheless, its use in dynamic pattern recognition have been much more restricted ...
D. Tomassi, Diego H. Milone, L. Forzani
ICML
2001
IEEE
15 years 10 months ago
Continuous-Time Hierarchical Reinforcement Learning
Hierarchical reinforcement learning (RL) is a general framework which studies how to exploit the structure of actions and tasks to accelerate policy learning in large domains. Pri...
Mohammad Ghavamzadeh, Sridhar Mahadevan
ACL
2003
14 years 11 months ago
Self-Organizing Markov Models and Their Application to Part-of-Speech Tagging
This paper presents a method to develop a class of variable memory Markov models that have higher memory capacity than traditional (uniform memory) Markov models. The structure of...
Jin-Dong Kim, Hae-Chang Rim, Jun-ichi Tsujii
CATA
2003
14 years 11 months ago
A Restaurant Finder using Belief-Desire-Intention Agent Model and Java Technology
It is becoming more important to design systems capable of performing high-level management and control tasks in interactive dynamic environments. At the same time, it is difficul...
Dongqing Lin, Thomas P. Wiggen, Chang-Hyun Jo