Learning from Words and Documents: Do the Same Topics

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Date: 2003-11-05 12:03:29 Supervised learning Metadata Unsupervised learning Information Learning Data Human–computer interaction Machine learning Knowledge representation Technical communication		Learning from Words and Documents: Do the Same Topics Add to Reading List Source URL: ils.unc.edu Download Document from Source Website File Size: 524,58 KB Share Document on Facebook

	Estimating the strength of unlabeled information during semi-supervised learning Brenden M. Lake () Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology James L. McClelland (m DocID: 1xVKT - View Document
	Natural Language to Structured Query Generation via Meta-Learning Po-Sen Huang 1 Chenglong Wang 2 Rishabh Singh 3 * Wen-tau Yih 4 Xiaodong He 5 * 1. Introduction Conventional supervised training is a pervasive paradigm DocID: 1xUL3 - View Document
	MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.merl.com Semi-Supervised Transfer Learning Using Marginal Predictors Deshmukh, A.; Laftchiev, E. DocID: 1vqKH - View Document
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	Online Semi-Supervised Learning on Quantized Graphs Michal Valko Branislav Kveton Computer Science Department Intel Labs DocID: 1vkLD - View Document