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ECEASST
2006
103views more  ECEASST 2006»
15 years 6 months ago
Petri Nets and Matrix Graph Grammars: Reachability
This paper contributes in two directions. First, several concepts of our matrix approach to graph transformation [1,2] such as coherence and minimal initial digraph are applied to ...
Juan de Lara, Pedro Pablo Pérez Velasco
CVPR
2009
IEEE
17 years 1 months ago
Dictionary-Free Categorization of Very Similar Objects via Stacked Evidence Trees
Current work in object categorization discriminates among objects that typically possess gross differences which are readily apparent. However, many applications require making ...
Andrew Moldenke, Asako Yamamuro, David A. Lytle, E...
CVPR
2008
IEEE
16 years 8 months ago
Max Margin AND/OR Graph learning for parsing the human body
We present a novel structure learning method, Max Margin AND/OR Graph (MM-AOG), for parsing the human body into parts and recovering their poses. Our method represents the human b...
Long Zhu, Yuanhao Chen, Yifei Lu, Chenxi Lin, Alan...
ICCV
2007
IEEE
16 years 8 months ago
An Invariant Large Margin Nearest Neighbour Classifier
The k-nearest neighbour (kNN) rule is a simple and effective method for multi-way classification that is much used in Computer Vision. However, its performance depends heavily on ...
M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserma...
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ICPR
2000
IEEE
16 years 7 months ago
General Bias/Variance Decomposition with Target Independent Variance of Error Functions Derived from the Exponential Family of D
An important theoretical tool in machine learning is the bias/variance decomposition of the generalization error. It was introduced for the mean square error in [3]. The bias/vari...
Jakob Vogdrup Hansen, Tom Heskes