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ICCV
2005
IEEE
14 years 9 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
MM
2006
ACM
157views Multimedia» more  MM 2006»
14 years 1 months ago
Player action recognition in broadcast tennis video with applications to semantic analysis of sports game
Recognition of player actions in broadcast sports video is a challenging task due to low resolution of the players in video frames. In this paper, we present a novel method to rec...
Guangyu Zhu, Changsheng Xu, Qingming Huang, Wen Ga...
ICIAP
2009
ACM
14 years 2 months ago
Towards a Theoretical Framework for Learning Multi-modal Patterns for Embodied Agents
Multi-modality is a fundamental feature that characterizes biological systems and lets them achieve high robustness in understanding skills while coping with uncertainty. Relativel...
Nicoletta Noceti, Barbara Caputo, Claudio Castelli...
KDD
2007
ACM
168views Data Mining» more  KDD 2007»
14 years 8 months ago
A probabilistic framework for relational clustering
Relational clustering has attracted more and more attention due to its phenomenal impact in various important applications which involve multi-type interrelated data objects, such...
Bo Long, Zhongfei (Mark) Zhang, Philip S. Yu
PR
2006
89views more  PR 2006»
13 years 7 months ago
Gaussian fields for semi-supervised regression and correspondence learning
Gaussian fields (GF) have recently received considerable attention for dimension reduction and semi-supervised classification. In this paper we show how the GF framework can be us...
Jakob J. Verbeek, Nikos A. Vlassis