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» Using State Modules for Adaptive Query Processing
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NIPS
1998
13 years 10 months ago
Risk Sensitive Reinforcement Learning
In this paper, we consider Markov Decision Processes (MDPs) with error states. Error states are those states entering which is undesirable or dangerous. We define the risk with re...
Ralph Neuneier, Oliver Mihatsch
SIGMETRICS
2005
ACM
120views Hardware» more  SIGMETRICS 2005»
14 years 2 months ago
Managing server energy and operational costs in hosting centers
The growing cost of tuning and managing computer systems is leading to out-sourcing of commercial services to hosting centers. These centers provision thousands of dense servers w...
Yiyu Chen, Amitayu Das, Wubi Qin, Anand Sivasubram...
RSS
2007
136views Robotics» more  RSS 2007»
13 years 10 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. ...
VLDB
2002
ACM
161views Database» more  VLDB 2002»
13 years 8 months ago
Distributed Search over the Hidden Web: Hierarchical Database Sampling and Selection
Many valuable text databases on the web have non-crawlable contents that are "hidden" behind search interfaces. Metasearchers are helpful tools for searching over many s...
Panagiotis G. Ipeirotis, Luis Gravano
DKE
2007
130views more  DKE 2007»
13 years 8 months ago
Enabling access-privacy for random walk based data analysis applications
Random walk graph and Markov chain based models are used heavily in many data and system analysis domains, including web, bioinformatics, and queuing. These models enable the desc...
Ping Lin, K. Selçuk Candan