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» Boosting Methods for Regression
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NIPS
2003
13 years 8 months ago
On the Dynamics of Boosting
In order to understand AdaBoost’s dynamics, especially its ability to maximize margins, we derive an associated simplified nonlinear iterated map and analyze its behavior in lo...
Cynthia Rudin, Ingrid Daubechies, Robert E. Schapi...
JMLR
2010
118views more  JMLR 2010»
13 years 2 months ago
Dirichlet Process Mixtures of Generalized Linear Models
We propose Dirichlet Process mixtures of Generalized Linear Models (DP-GLMs), a new method of nonparametric regression that accommodates continuous and categorical inputs, models ...
Lauren Hannah, David M. Blei, Warren B. Powell
ICPR
2004
IEEE
14 years 8 months ago
Resolution Enhancement by AdaBoost
This paper proposes a learning scheme based still image super-resolution reconstruction algorithm. Superresolution reconstruction is proposed as a binary classification problem an...
Bhaskar D. Rao, Junwen Wu, Mohan M. Trivedi
CVPR
2009
IEEE
15 years 2 months ago
Granularity-tunable Gradients Partition (GGP) Descriptors for Human Detection
This paper proposes a novel descriptor, granularitytunable gradients partition (GGP), for human detection. The concept granularity is used to define the spatial and angular unce...
Yazhou Liu (Harbin Institute of Technology), Shigu...
IJCAI
2007
13 years 8 months ago
Logistic Regression Models for a Fast CBIR Method Based on Feature Selection
Distance measures like the Euclidean distance have been the most widely used to measure similarities between feature vectors in the content-based image retrieval (CBIR) systems. H...
Riadh Ksantini, Djemel Ziou, Bernard Colin, Fran&c...