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» Learning to Recognize Objects with Little Supervision
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CVPR
2007
IEEE
14 years 7 months ago
Composite Models of Objects and Scenes for Category Recognition
This paper presents a method of learning and recognizing generic object categories using part-based spatial models. The models are multiscale, with a scene component that specifie...
David J. Crandall, Daniel P. Huttenlocher
CVPR
2009
IEEE
15 years 12 days ago
Unsupervised Learning for Graph Matching
Graph matching is an important problem in computer vision. It is used in 2D and 3D object matching and recognition. Despite its importance, there is little literature on learnin...
Marius Leordeanu, Martial Hebert
ICML
2009
IEEE
14 years 6 months ago
Proximal regularization for online and batch learning
Many learning algorithms rely on the curvature (in particular, strong convexity) of regularized objective functions to provide good theoretical performance guarantees. In practice...
Chuong B. Do, Quoc V. Le, Chuan-Sheng Foo
IJCSA
2008
104views more  IJCSA 2008»
13 years 4 months ago
Artificial Intelligence and Bluetooth Techniques in a Multi-user M-learning Domain
In this paper we present a practical implementation of a multiuser technical laboratory that combines Artificial Intelligence (AI) and Bluetooth (BT) techniques. The objective is ...
Bonifacio Castaño, Angel Moreno, Melquiades...
IJCNN
2008
IEEE
13 years 11 months ago
A neural wake-sleep learning architecture for associating robotic facial emotions
—A novel wake-sleep learning architecture for processing a robot’s facial expressions is introduced. According to neuroscience evidence, associative learning of emotional respo...
Chi-Yung Yau, Kevin Burn, Stefan Wermter