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» Predicting relative performance of classifiers from samples
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NIPS
2001
13 years 10 months ago
Sequential Noise Compensation by Sequential Monte Carlo Method
We present a sequential Monte Carlo method applied to additive noise compensation for robust speech recognition in time-varying noise. The method generates a set of samples accord...
K. Yao, S. Nakamura
ESANN
2006
13 years 10 months ago
Margin based Active Learning for LVQ Networks
In this article, we extend a local prototype-based learning model by active learning, which gives the learner the capability to select training samples during the model adaptation...
Frank-Michael Schleif, Barbara Hammer, Thomas Vill...
SDM
2007
SIAM
177views Data Mining» more  SDM 2007»
13 years 10 months ago
Multi-way Clustering on Relation Graphs
A number of real-world domains such as social networks and e-commerce involve heterogeneous data that describes relations between multiple classes of entities. Understanding the n...
Arindam Banerjee, Sugato Basu, Srujana Merugu
AAMAS
2005
Springer
13 years 9 months ago
Learning and Exploiting Relative Weaknesses of Opponent Agents
Agents in a competitive interaction can greatly benefit from adapting to a particular adversary, rather than using the same general strategy against all opponents. One method of s...
Shaul Markovitch, Ronit Reger
WWW
2011
ACM
13 years 4 months ago
Information credibility on twitter
We analyze the information credibility of news propagated through Twitter, a popular microblogging service. Previous research has shown that most of the messages posted on Twitter...
Carlos Castillo, Marcelo Mendoza, Barbara Poblete