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» Learning Classifiers from Semantically Heterogeneous Data
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IJCAI
1989
13 years 8 months ago
An Experimental Comparison of Symbolic and Connectionist Learning Algorithms
Despite the fact that many symbolic and connectionist (neural net) learning algorithms are addressing the same problem of learning from classified examples, very little Is known r...
Raymond J. Mooney, Jude W. Shavlik, Geoffrey G. To...
COOPIS
1998
IEEE
13 years 12 months ago
Context Mediation on Wall Street
This paper reports on a practical implementation of a context mediator for the fixed income securities industry. We describe industry circumstances and the data and calculation se...
Allen Moulton, Stuart E. Madnick, Michael Siegel
SEMWEB
2005
Springer
14 years 1 months ago
Gnowsis Adapter Framework: Treating Structured Data Sources as Virtual RDF Graphs
The integration of heterogenous data sources is a crucial step for the upcoming semantic web – if existing information is not integrated, where will the data come from that the s...
Leo Sauermann, Sven Schwarz
ICASSP
2010
IEEE
13 years 7 months ago
Multi-View Object Detection by Classifier Interpolation
In this paper, we propose a novel solution for multi-view object detection. Given a set of training examples at different views, we select examples at a few key views and train on...
Xiaobai Liu, Haifeng Gong, Shuicheng Yan, Hai Jin
EMNLP
2008
13 years 9 months ago
Modeling Annotators: A Generative Approach to Learning from Annotator Rationales
A human annotator can provide hints to a machine learner by highlighting contextual "rationales" for each of his or her annotations (Zaidan et al., 2007). How can one ex...
Omar Zaidan, Jason Eisner