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» A Preference Model for Structured Supervised Learning Tasks
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CVPR
2008
IEEE
15 years 12 months ago
A hierarchical and contextual model for aerial image understanding
In this paper we present a novel method for parsing aerial images with a hierarchical and contextual model learned in a statistical framework. We learn hierarchies at the scene an...
Jake Porway, Kristy Wang, Benjamin Yao, Song Chun ...
ICML
2005
IEEE
15 years 10 months ago
Reducing overfitting in process model induction
In this paper, we review the paradigm of inductive process modeling, which uses background knowledge about possible component processes to construct quantitative models of dynamic...
Will Bridewell, Narges Bani Asadi, Pat Langley, Lj...
ACII
2011
Springer
13 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...
UAI
2003
14 years 11 months ago
On the Convergence of Bound Optimization Algorithms
Many practitioners who use EM and related algorithms complain that they are sometimes slow. When does this happen, and what can be done about it? In this paper, we study the gener...
Ruslan Salakhutdinov, Sam T. Roweis, Zoubin Ghahra...
BMCBI
2006
137views more  BMCBI 2006»
14 years 10 months ago
A classification-based framework for predicting and analyzing gene regulatory response
Background: We have recently introduced a predictive framework for studying gene transcriptional regulation in simpler organisms using a novel supervised learning algorithm called...
Anshul Kundaje, Manuel Middendorf, Mihir Shah, Chr...