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» Hierarchical Mixture Models for Nested Data Structures
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135
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IJRR
2007
186views more  IJRR 2007»
15 years 3 months ago
Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
Learning patterns of human behavior from sensor data is extremely important for high-level activity inference. We show how to extract a person’s activities and significant plac...
Lin Liao, Dieter Fox, Henry A. Kautz
IJCNN
2000
IEEE
15 years 7 months ago
Regression Analysis for Rival Penalized Competitive Learning Binary Tree
The main aim of this paper is to develop a suitable regression analysis model for describing the relationship between the index efficiency and the parameters of the Rival Penaliz...
Xuequn Li, Irwin King
TFS
2008
174views more  TFS 2008»
15 years 3 months ago
Type-2 Fuzzy Markov Random Fields and Their Application to Handwritten Chinese Character Recognition
In this paper, we integrate type-2 (T2) fuzzy sets with Markov random fields (MRFs) referred to as T2 FMRFs, which may handle both fuzziness and randomness in the structural patter...
Jia Zeng, Zhi-Qiang Liu
115
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ML
2002
ACM
100views Machine Learning» more  ML 2002»
15 years 3 months ago
Structure in the Space of Value Functions
Solving in an efficient manner many different optimal control tasks within the same underlying environment requires decomposing the environment into its computationally elemental ...
David J. Foster, Peter Dayan
MKM
2009
Springer
15 years 10 months ago
From Tessellations to Table Interpretation
The extraction of the relations of nested table headers to content cells is automated with a view to constructing narrow domain ontologies of semistructured web data. A taxonomy of...
Ramana C. Jandhyala, Mukkai S. Krishnamoorthy, Geo...