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195
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ICS
2010
Tsinghua U.
16 years 4 months ago
Robustness of the Learning with Errors Assumption
: Starting with the work of Ishai-Sahai-Wagner and Micali-Reyzin, a new goal has been set within the theory of cryptography community, to design cryptographic primitives that are s...
Shafi Goldwasser, Yael Kalai, Chris Peikert, Vinod...
182
Voted
IROS
2009
IEEE
150views Robotics» more  IROS 2009»
16 years 1 months ago
Learning locomotion over rough terrain using terrain templates
— We address the problem of foothold selection in robotic legged locomotion over very rough terrain. The difficulty of the problem we address here is comparable to that of human...
Mrinal Kalakrishnan, Jonas Buchli, Peter Pastor, S...
GECCO
2005
Springer
119views Optimization» more  GECCO 2005»
16 years 23 days ago
Learning, anticipation and time-deception in evolutionary online dynamic optimization
In this paper we focus on an important source of problem– difficulty in (online) dynamic optimization problems that has so far received significantly less attention than the tr...
Peter A. N. Bosman
184
Voted
PAM
2005
Springer
16 years 22 days ago
Self-Learning IP Traffic Classification Based on Statistical Flow Characteristics
A number of key areas in IP network engineering, management and surveillance greatly benefit from the ability to dynamically identify traffic flows according to the applications re...
Sebastian Zander, Thuy T. T. Nguyen, Grenville J. ...
194
Voted
DMIN
2007
186views Data Mining» more  DMIN 2007»
15 years 8 months ago
Cost-Sensitive Learning vs. Sampling: Which is Best for Handling Unbalanced Classes with Unequal Error Costs?
- The classifier built from a data set with a highly skewed class distribution generally predicts the more frequently occurring classes much more often than the infrequently occurr...
Gary M. Weiss, Kate McCarthy, Bibi Zabar