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» Evaluating learning algorithms and classifiers
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KDD
2009
ACM
230views Data Mining» more  KDD 2009»
16 years 3 months ago
Cross domain distribution adaptation via kernel mapping
When labeled examples are limited and difficult to obtain, transfer learning employs knowledge from a source domain to improve learning accuracy in the target domain. However, the...
ErHeng Zhong, Wei Fan, Jing Peng, Kun Zhang, Jiang...
ANSS
2006
IEEE
15 years 9 months ago
Performance Study of End-to-End Traffic-Aware Routing
There has been a lot research effort on developing reactive routing algorithms for mobile ad hoc networks (MANETs) over the past few years. Most of these algorithms consider findi...
Raad S. Al-Qassas, Lewis M. Mackenzie, Mohamed Oul...
FAST
2008
15 years 4 months ago
Enhancing Storage System Availability on Multi-Core Architectures with Recovery-Conscious Scheduling
In this paper we develop a recovery conscious framework for multi-core architectures and a suite of techniques for improving the resiliency and recovery efficiency of highly conc...
Sangeetha Seshadri, Lawrence Chiu, Cornel Constant...
UAI
2004
15 years 4 months ago
Active Model Selection
Classical learning assumes the learner is given a labeled data sample, from which it learns a model. The field of Active Learning deals with the situation where the learner begins...
Omid Madani, Daniel J. Lizotte, Russell Greiner
ECAI
2006
Springer
15 years 6 months ago
Cheating Is Not Playing: Methodological Issues of Computational Game Theory
Abstract. Computational Game Theory is a way to study and evaluate behaviors using game theory models, via agent-based computer simulations. One of the most known example of this a...
Bruno Beaufils, Philippe Mathieu