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» Structured Learning and Prediction in Computer Vision
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ICML
2000
IEEE
14 years 10 months ago
Learning Bayesian Networks for Diverse and Varying numbers of Evidence Sets
We introduce an expandable Bayesian network (EBN) to handle the combination of diverse multiple homogeneous evidence sets. An EBN is an augmented Bayesian network which instantiat...
Zu Whan Kim, Ramakant Nevatia
ACII
2011
Springer
12 years 9 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...
CVPR
2004
IEEE
14 years 11 months ago
Unsupervised Learning of Image Manifolds by Semidefinite Programming
Can we detect low dimensional structure in high dimensional data sets of images? In this paper, we propose an algorithm for unsupervised learning of image manifolds by semidefinit...
Kilian Q. Weinberger, Lawrence K. Saul
BMCBI
2008
114views more  BMCBI 2008»
13 years 9 months ago
Combining classifiers for improved classification of proteins from sequence or structure
Background: Predicting a protein's structural or functional class from its amino acid sequence or structure is a fundamental problem in computational biology. Recently, there...
Iain Melvin, Jason Weston, Christina S. Leslie, Wi...
RECOMB
2001
Springer
14 years 9 months ago
Predicting the beta-helix fold from protein sequence data
A method is presented that uses b-strand interactions to predict the parallel right-handed b-helix super-secondary structural motif in protein sequences. A program called BetaWrap...
Phil Bradley, Lenore Cowen, Matthew Menke, Jonatha...