% This file was created with JabRef 2.8. % Encoding: Cp1250 @STRING{IEEENN = {IEEE Transaction on Neural Networks}} @STRING{MIT = {MIT Press}} @STRING{NC = {Neural Computation}} @STRING{NIPS = {Advances in Neural Information Processing Systems }} @STRING{NN = {Neural Networks}} @STRING{NPL = {Neural Processing Letters}} @STRING{pieee = {Proceedings of the {IEEE}}} @STRING{Springer = {Springer-Verlag}} @INPROCEEDINGS{EigenTransfer09, author = {Wenyuan Dai and Ou Jin and Gui-Rong Xue and Qiang Yang and Yong Yu}, title = {EigenTransfer: a unified framework for transfer learning}, booktitle = {ICML}, year = {2009}, pages = {25}, bibsource = {DBLP, http://dblp.uni-trier.de}, crossref = {DBLP:conf/icml/2009}, ee = {http://doi.acm.org/10.1145/1553374.1553399} } @INPROCEEDINGS{WuD04, author = {Pengcheng Wu and Thomas G. 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We present a computational model that predicts the functional magnetic resonance imaging (fMRI) neural activation associated with words for which fMRI data are not yet available. This model is trained with a combination of data from a trillion-word text corpus and observed fMRI data associated with viewing several dozen concrete nouns. Once trained, the model predicts fMRI activation for thousands of other concrete nouns in the text corpus, with highly significant accuracies over the 60 nouns for which we currently have fMRI data. 10.1126/science.1152876}, citeulike-article-id = {2845964}, doi = {http://dx.doi.org/10.1126/science.1152876}, keywords = {brain, categories, computational, fmri, model, nouns, semantic}, owner = {Duch}, posted-at = {2008-06-07 09:45:27}, priority = {2}, timestamp = {2008.11.06}, url = {http://dx.doi.org/10.1126/science.1152876} } @BOOK{Mitra03, title = {Data Mining: Multimedia, Soft Computing, and Bioinformatics}, publisher = {J. 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