Sciweavers

16 search results - page 3 / 4
» Multi-agent reward analysis for learning in noisy domains
Sort
View
JMLR
2010
172views more  JMLR 2010»
13 years 1 months ago
Modeling annotator expertise: Learning when everybody knows a bit of something
Supervised learning from multiple labeling sources is an increasingly important problem in machine learning and data mining. This paper develops a probabilistic approach to this p...
Yan Yan, Rómer Rosales, Glenn Fung, Mark W....
CORR
2010
Springer
193views Education» more  CORR 2010»
13 years 4 months ago
A Probabilistic Approach for Learning Folksonomies from Structured Data
Learning structured representations has emerged as an important problem in many domains, including document and Web data mining, bioinformatics, and image analysis. One approach t...
Anon Plangprasopchok, Kristina Lerman, Lise Getoor
TASLP
2010
138views more  TASLP 2010»
13 years 29 days ago
Glimpsing IVA: A Framework for Overcomplete/Complete/Undercomplete Convolutive Source Separation
Abstract--Independent vector analysis (IVA) is a method for separating convolutedly mixed signals that significantly reduces the occurrence of the well-known permutation problem in...
Alireza Masnadi-Shirazi, Wenyi Zhang, Bhaskar D. R...
SYNTHESE
2008
84views more  SYNTHESE 2008»
13 years 6 months ago
How experimental algorithmics can benefit from Mayo's extensions to Neyman-Pearson theory of testing
Although theoretical results for several algorithms in many application domains were presented during the last decades, not all algorithms can be analyzed fully theoretically. Exp...
Thomas Bartz-Beielstein
WWW
2007
ACM
14 years 7 months ago
Organizing and searching the world wide web of facts -- step two: harnessing the wisdom of the crowds
As part of a large effort to acquire large repositories of facts from unstructured text on the Web, a seed-based framework for textual information extraction allows for weakly sup...
Marius Pasca