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» An Introduction to Learning Structured Information
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VLSISP
1998
111views more  VLSISP 1998»
15 years 3 months ago
Quantitative Analysis of MR Brain Image Sequences by Adaptive Self-Organizing Finite Mixtures
This paper presents an adaptive structure self-organizing finite mixture network for quantification of magnetic resonance (MR) brain image sequences. We present justification fo...
Yue Wang, Tülay Adali, Chi-Ming Lau, Sun-Yuan...
JMLR
2010
119views more  JMLR 2010»
14 years 10 months ago
Factorized Orthogonal Latent Spaces
Existing approaches to multi-view learning are particularly effective when the views are either independent (i.e, multi-kernel approaches) or fully dependent (i.e., shared latent ...
Mathieu Salzmann, Carl Henrik Ek, Raquel Urtasun, ...
ACII
2011
Springer
14 years 3 months ago
Predicting Facial Indicators of Confusion with Hidden Markov Models
Affect plays a vital role in learning. During tutoring, particular affective states may benefit or detract from student learning. A key cognitiveaffective state is confusion, which...
Joseph F. Grafsgaard, Kristy Elizabeth Boyer, Jame...
CIKM
2009
Springer
15 years 10 months ago
Incremental query evaluation for support vector machines
Support vector machines (SVMs) have been widely used in multimedia retrieval to learn a concept in order to find the best matches. In such a SVM active learning environment, the ...
Danzhou Liu, Kien A. Hua
SECON
2008
IEEE
15 years 10 months ago
Practical Algorithms for Gathering Stored Correlated Data in a Network
—Many sensing systems remotely monitor/measure an environment at several sites, and then report these observations to a central site. We propose and investigate several practical...
Ramin Khalili, James F. Kurose