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ICML
2002
IEEE
14 years 7 months ago
Hierarchically Optimal Average Reward Reinforcement Learning
Two notions of optimality have been explored in previous work on hierarchical reinforcement learning (HRL): hierarchical optimality, or the optimal policy in the space defined by ...
Mohammad Ghavamzadeh, Sridhar Mahadevan
ICML
2005
IEEE
14 years 7 months ago
Learning predictive representations from a history
Predictive State Representations (PSRs) have shown a great deal of promise as an alternative to Markov models. However, learning a PSR from a single stream of data generated from ...
Eric Wiewiora
TREC
2004
13 years 8 months ago
Can We Get A Better Retrieval Function From Machine?
The quality of an information retrieval system heavily depends on its retrieval function, which returns a similarity measurement between the query and each document in the collect...
Weiguo Fan, Wensi Xi, Edward A. Fox, Li Wang
IJCAI
2007
13 years 8 months ago
Constructing New and Better Evaluation Measures for Machine Learning
Evaluation measures play an important role in machine learning because they are used not only to compare different learning algorithms, but also often as goals to optimize in cons...
Jin Huang, Charles X. Ling
ECIR
2010
Springer
13 years 4 months ago
Maximum Margin Ranking Algorithms for Information Retrieval
Abstract. Machine learning ranking methods are increasingly applied to ranking tasks in information retrieval (IR). However ranking tasks in IR often differ from standard ranking t...
Shivani Agarwal, Michael Collins