Inactive Learning? Difficulties Employing Active Learning in Practice Josh Attenberg Foster Provost

First Page		Document Content
Date: 2011-04-05 14:01:36 Learning Supervised learning Active learning Overfitting Support vector machine Semi-supervised learning Concept learning Machine learning Education Statistics		Inactive Learning? Difficulties Employing Active Learning in Practice Josh Attenberg Foster Provost Add to Reading List Source URL: pages.stern.nyu.edu Download Document from Source Website File Size: 218,32 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
	Weakly-Supervised Grammar-Informed Bayesian CCG Parser Learning Dan Garrette Chris Dyer Jason Baldridge DocID: 1vlce - View Document
	Online Semi-Supervised Learning on Quantized Graphs Michal Valko Branislav Kveton Computer Science Department Intel Labs DocID: 1vkLD - View Document