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» Learning Models for Object Recognition
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135
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KDD
2004
ACM
166views Data Mining» more  KDD 2004»
16 years 4 months ago
Predicting prostate cancer recurrence via maximizing the concordance index
In order to effectively use machine learning algorithms, e.g., neural networks, for the analysis of survival data, the correct treatment of censored data is crucial. The concordan...
Lian Yan, David Verbel, Olivier Saidi
112
Voted
AAAI
2000
15 years 5 months ago
Self-Organization of Innate Face Preferences: Could Genetics Be Expressed through Learning?
Self-organizing models develop realistic cortical structures when given approximations of the visual environment as input, and are an effective way to model the development of fac...
James A. Bednar, Risto Miikkulainen
ICCV
2001
IEEE
16 years 5 months ago
Learning Image Statistics for Bayesian Tracking
This paper describes a framework for learning probabilistic models of objects and scenes and for exploiting these models for tracking complex, deformable, or articulated objects i...
Hedvig Sidenbladh, Michael J. Black
126
Voted
ICMCS
2009
IEEE
168views Multimedia» more  ICMCS 2009»
15 years 1 months ago
Human activity recognition based on the blob features
In this paper, we present a novel approach for human activities recognition in the video. We analyze human activities in the sequential frames because human activities can be cons...
Jie Yang, Jian Cheng, Hanqing Lu
240
Voted
EJASMP
2011
14 years 7 months ago
Phoneme and Sentence-Level Ensembles for Speech Recognition
We address the question of whether and how boosting and bagging can be used for speech recognition. In order to do this, we compare two different boosting schemes, one at the pho...
Christos Dimitrakakis, Samy Bengio