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» Learning Models for Object Recognition
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NN
1997
Springer
174views Neural Networks» more  NN 1997»
15 years 10 months ago
Learning Dynamic Bayesian Networks
Bayesian networks are directed acyclic graphs that represent dependencies between variables in a probabilistic model. Many time series models, including the hidden Markov models (H...
Zoubin Ghahramani
ICB
2007
Springer
139views Biometrics» more  ICB 2007»
15 years 10 months ago
Tracking and Recognition of Multiple Faces at Distances
Many applications require tracking and recognition of multiple faces at distances, such as in video surveillance. Such a task, dealing with non-cooperative objects is more challeng...
Rong Liu, Xiufeng Gao, Rufeng Chu, XiangXin Zhu, S...
ICML
2003
IEEE
16 years 7 months ago
Hidden Markov Support Vector Machines
This paper presents a novel discriminative learning technique for label sequences based on a combination of the two most successful learning algorithms, Support Vector Machines an...
Yasemin Altun, Ioannis Tsochantaridis, Thomas Hofm...
ICML
2007
IEEE
16 years 7 months ago
Learning to rank: from pairwise approach to listwise approach
The paper is concerned with learning to rank, which is to construct a model or a function for ranking objects. Learning to rank is useful for document retrieval, collaborative fil...
Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, Han...
CVPR
2004
IEEE
16 years 8 months ago
Automatic View Recognition in Echocardiogram Videos Using Parts-Based Representation
Indexing echocardiogram videos at different levels of structure is essential for providing efficient access to their content for browsing and retrieval purposes. We present a nove...
Shahram Ebadollahi, Shih-Fu Chang, Henry Wu