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8 years 8 months ago
Approximately optimal adaptive learning in opportunistic spectrum access
—In this paper we develop an adaptive learning algorithm which is approximately optimal for an opportunistic spectrum access (OSA) problem with polynomial complexity. In this OSA...
Cem Tekin, Mingyan Liu
142views Data Mining» more  DMIN 2009»
10 years 4 months ago
Action Selection in Customer Value Optimization: An Approach Based on Covariate-Dependent Markov Decision Processes
Typical methods in CRM marketing include action selection on the basis of Markov Decision Processes with fixed transition probabilities on the one hand, and scoring customers separ...
Angi Roesch, Harald Schmidbauer
85views more  IJVR 2007»
10 years 6 months ago
Detection of Landmarks for Clustering of Online-Game Players
—Understanding of player behaviors is an important issue to keep online games interesting to their players. Focusing on player movement, in our previous work, we proposed a metho...
Ruck Thawonmas, Masayoshi Kurashige, Kuan-Ta Chen
10 years 7 months ago
Efficient Solutions to Factored MDPs with Imprecise Transition Probabilities
When modeling real-world decision-theoretic planning problems in the Markov decision process (MDP) framework, it is often impossible to obtain a completely accurate estimate of tr...
Karina Valdivia Delgado, Scott Sanner, Leliane Nun...
10 years 7 months ago
Stochastic Analysis of Qualitative Dynamics
We extend qualitative reasoning with estimations of the relative likelihoods of the pos sible qualitative behaviors . We estimate the likelihoods by viewing the dynamics o f a sys...
Jon Doyle, Elisha Sacks
108views more  ANLP 1994»
10 years 7 months ago
Does Baum-Welch Re-estimation Help Taggers?
In part of speech tagging by Hidden Markov Model, a statistical model is used to assign grammatical categories to words in a text. Early work in the field relied on a corpus which...
David Elworthy
10 years 7 months ago
Spike Sorting: Bayesian Clustering of Non-Stationary Data
Spike sorting involves clustering spike trains recorded by a microelectrode according to the source neuron. It is a complicated problem, which requires a lot of human labor, partl...
Aharon Bar-Hillel, Adam Spiro, Eran Stark
136views Robotics» more  RSS 2007»
10 years 7 months ago
The Stochastic Motion Roadmap: A Sampling Framework for Planning with Markov Motion Uncertainty
— We present a new motion planning framework that explicitly considers uncertainty in robot motion to maximize the probability of avoiding collisions and successfully reaching a ...
Ron Alterovitz, Thierry Siméon, Kenneth Y. ...
10 years 7 months ago
Optimistic Linear Programming gives Logarithmic Regret for Irreducible MDPs
We present an algorithm called Optimistic Linear Programming (OLP) for learning to optimize average reward in an irreducible but otherwise unknown Markov decision process (MDP). O...
Ambuj Tewari, Peter L. Bartlett
10 years 10 months ago
Exact and Approximate Methods for Calculating Signal and Transition Probabilities in FSMs
In this paper, we consider the problem of calculating the signal and transition probabilities of the internal nodes of the combinational logic part of a nite state machine (FSM). ...
Chi-Ying Tsui, Massoud Pedram, Alvin M. Despain