<--- Back to Details
First PageDocument Content
Machine learning / Semi-supervised learning / Labeled data / Supervised learning
Date: 2011-05-20 18:34:42
Machine learning
Semi-supervised learning
Labeled data
Supervised learning

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

Add to Reading List

Source URL: cims.nyu.edu

Download Document from Source Website

File Size: 417,60 KB

Share Document on Facebook

Similar Documents

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

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

MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.merl.com Semi-Supervised Transfer Learning Using Marginal Predictors Deshmukh, A.; Laftchiev, E.

MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.merl.com Semi-Supervised Transfer Learning Using Marginal Predictors Deshmukh, A.; Laftchiev, E.

DocID: 1vqKH - View Document

Online Semi-Supervised Learning on Quantized Graphs  Michal Valko Branislav Kveton Computer Science Department Intel Labs

Online Semi-Supervised Learning on Quantized Graphs Michal Valko Branislav Kveton Computer Science Department Intel Labs

DocID: 1vkLD - View Document

Semi-Supervised Learning with the Deep Rendering Mixture Model Tan Nguyen1,2 Wanjia Liu1 Ethan Perez1 Richard G. Baraniuk1

Semi-Supervised Learning with the Deep Rendering Mixture Model Tan Nguyen1,2 Wanjia Liu1 Ethan Perez1 Richard G. Baraniuk1

DocID: 1v26R - View Document

A human motion feature based on semi-supervised learning of GMM

A human motion feature based on semi-supervised learning of GMM

DocID: 1uXNi - View Document