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» Boosting Methods for Regression
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ICCV
2005
IEEE
14 years 3 months ago
TemporalBoost for Event Recognition
This paper contributes a new boosting paradigm to achieve detection of events in video. Previous boosting paradigms in vision focus on single frame detection and do not scale to v...
Paul Smith, Niels da Vitoria Lobo, Mubarak Shah
PAKDD
2004
ACM
137views Data Mining» more  PAKDD 2004»
14 years 3 months ago
Fast and Light Boosting for Adaptive Mining of Data Streams
Supporting continuous mining queries on data streams requires algorithms that (i) are fast, (ii) make light demands on memory resources, and (iii) are easily to adapt to concept dr...
Fang Chu, Carlo Zaniolo
ICMCS
2009
IEEE
189views Multimedia» more  ICMCS 2009»
13 years 7 months ago
Emotion recognition from speech VIA boosted Gaussian mixture models
Gaussian mixture models (GMMs) and the minimum error rate classifier (i.e. Bayesian optimal classifier) are popular and effective tools for speech emotion recognition. Typically, ...
Hao Tang, Stephen M. Chu, Mark Hasegawa-Johnson, T...
CIMAGING
2008
172views Hardware» more  CIMAGING 2008»
13 years 11 months ago
A generalization of non-local means via kernel regression
The Non-Local Means (NLM) method of denoising has received considerable attention in the image processing community due to its performance, despite its simplicity. In this paper, ...
Priyam Chatterjee, Peyman Milanfar
ICCV
2001
IEEE
14 years 11 months ago
Shape Deformation: SVM Regression and Application to Medical Image Segmentation
This paper presents a novel landmark-based shape deformation method. This method effectively solves two problems inherent in landmark-based shape deformation: (a) identification o...
Song Wang, Weiyu Zhu, Zhi-Pei Liang