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» Supervised Link Prediction Using Multiple Sources
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NAACL
2004
13 years 11 months ago
Predicting Emotion in Spoken Dialogue from Multiple Knowledge Sources
We examine the utility of multiple types of turn-level and contextual linguistic features for automatically predicting student emotions in human-human spoken tutoring dialogues. W...
Katherine Forbes-Riley, Diane J. Litman
EMNLP
2011
12 years 9 months ago
Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions
We introduce a novel machine learning framework based on recursive autoencoders for sentence-level prediction of sentiment label distributions. Our method learns vector space repr...
Richard Socher, Jeffrey Pennington, Eric H. Huang,...
KDD
2009
ACM
198views Data Mining» more  KDD 2009»
14 years 10 months ago
Heterogeneous source consensus learning via decision propagation and negotiation
Nowadays, enormous amounts of data are continuously generated not only in massive scale, but also from different, sometimes conflicting, views. Therefore, it is important to conso...
Jing Gao, Wei Fan, Yizhou Sun, Jiawei Han
DIS
2009
Springer
14 years 4 months ago
MICCLLR: Multiple-Instance Learning Using Class Conditional Log Likelihood Ratio
Multiple-instance learning (MIL) is a generalization of the supervised learning problem where each training observation is a labeled bag of unlabeled instances. Several supervised ...
Yasser El-Manzalawy, Vasant Honavar
DCC
2005
IEEE
14 years 9 months ago
Quantization of Multiple Sources Using Integer Bit Allocation
Asymptotically optimal bit allocation among a set of quantizers for a finite collection of sources was determined in 1963 by Huang and Schultheiss. Their solution, however, gives ...
Benjamin Farber, Kenneth Zeger