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ICCV
2005
IEEE
14 years 7 months ago
Probabilistic Boosting-Tree: Learning Discriminative Models for Classification, Recognition, and Clustering
In this paper, a new learning framework?probabilistic boosting-tree (PBT), is proposed for learning two-class and multi-class discriminative models. In the learning stage, the pro...
Zhuowen Tu
ICCV
2005
IEEE
13 years 11 months ago
LOCUS: Learning Object Classes with Unsupervised Segmentation
We address the problem of learning object class models and object segmentations from unannotated images. We introduce LOCUS (Learning Object Classes with Unsupervised Segmentation...
John M. Winn, Nebojsa Jojic
JMLR
2010
192views more  JMLR 2010»
13 years 17 days ago
Efficient Learning of Deep Boltzmann Machines
We present a new approximate inference algorithm for Deep Boltzmann Machines (DBM's), a generative model with many layers of hidden variables. The algorithm learns a separate...
Ruslan Salakhutdinov, Hugo Larochelle
UAI
2000
13 years 7 months ago
Variational Relevance Vector Machines
The Support Vector Machine (SVM) of Vapnik [9] has become widely established as one of the leading approaches to pattern recognition and machine learning. It expresses predictions...
Christopher M. Bishop, Michael E. Tipping
ECCV
2004
Springer
13 years 11 months ago
Pose Invariant Face Recognition Under Arbitrary Unknown Lighting Using Spherical Harmonics
Abstract. We propose a new method for face recognition under arbitrary pose and illumination conditions, which requires only one training image per subject. Furthermore, no limitat...
Lei Zhang 0002, Dimitris Samaras