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» Modeling Problem Transformations based on Data Complexity
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CVPR
2010
IEEE
15 years 9 months ago
Classification and Clustering via Dictionary Learning with Structured Incoherence
A clustering framework within the sparse modeling and dictionary learning setting is introduced in this work. Instead of searching for the set of centroid that best fit the data, ...
Pablo Sprechmann, Ignacio Ramirez, Guillermo Sapir...
COST
2010
Springer
194views Multimedia» more  COST 2010»
15 years 1 months ago
The MultiLis Corpus - Dealing with Individual Differences in Nonverbal Listening Behavior
Abstract. Computational models that attempt to predict when a virtual human should backchannel are often based on the analysis of recordings of face-to-face conversations between h...
Iwan de Kok, Dirk Heylen
KDD
2008
ACM
211views Data Mining» more  KDD 2008»
16 years 6 months ago
ArnetMiner: extraction and mining of academic social networks
This paper addresses several key issues in the ArnetMiner system, which aims at extracting and mining academic social networks. Specifically, the system focuses on: 1) Extracting ...
Jie Tang, Jing Zhang, Limin Yao, Juanzi Li, Li Zha...
WSDM
2012
ACM
254views Data Mining» more  WSDM 2012»
14 years 1 months ago
Maximizing product adoption in social networks
One of the key objectives of viral marketing is to identify a small set of users in a social network, who when convinced to adopt a product will influence others in the network l...
Smriti Bhagat, Amit Goyal 0002, Laks V. S. Lakshma...
WWW
2007
ACM
16 years 7 months ago
Compare&contrast: using the web to discover comparable cases for news stories
Comparing and contrasting is an important strategy people employ to understand new situations and create solutions for new problems. Similar events can provide hints for problem s...
Jiahui Liu, Earl Wagner, Larry Birnbaum
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