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» Evaluating learning algorithms and classifiers
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KDD
2009
ACM
230views Data Mining» more  KDD 2009»
14 years 9 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
14 years 3 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
13 years 10 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
13 years 10 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
14 years 17 days 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