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» Learning Classifiers from Semantically Heterogeneous Data
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VIP
2003
13 years 10 months ago
Using Dual Cascading Learning Frameworks for Image Indexing
To bridge the semantic gap in content-based image retrieval, detecting meaningful visual entities (e.g. faces, sky, foliage, buildings etc) in image content and classifying images...
Joo-Hwee Lim, Jesse S. Jin
ML
2000
ACM
124views Machine Learning» more  ML 2000»
13 years 8 months ago
Text Classification from Labeled and Unlabeled Documents using EM
This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. ...
Kamal Nigam, Andrew McCallum, Sebastian Thrun, Tom...
WWW
2008
ACM
14 years 9 months ago
A semantic layer for publishing and localizing xml data for a p2p xquery mediator
In this poster, we present the P2P XML data localization layer of XLive, an XQuery mediation system developed at University of Versailles [2]. A major challenge in the evaluation ...
Florin Dragan, Georges Gardarin, Laurent Yeh
JUCS
2008
196views more  JUCS 2008»
13 years 8 months ago
Information Integration for the Masses
Abstract: Information integration applications combine data from heterogeneous sources to assist the user in solving repetitive data-intensive tasks. Currently, such applications r...
Jim Blythe, Dipsy Kapoor, Craig A. Knoblock, Krist...
ICMLA
2010
13 years 6 months ago
Boosting Multi-Task Weak Learners with Applications to Textual and Social Data
Abstract--Learning multiple related tasks from data simultaneously can improve predictive performance relative to learning these tasks independently. In this paper we propose a nov...
Jean Baptiste Faddoul, Boris Chidlovskii, Fabien T...