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» Error Rejection in Linearly Combined Multiple Classifiers
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KDD
2007
ACM
152views Data Mining» more  KDD 2007»
14 years 8 months ago
Privacy-Preserving Sharing of Horizontally-Distributed Private Data for Constructing Accurate Classifiers
Data mining tasks such as supervised classification can often benefit from a large training dataset. However, in many application domains, privacy concerns can hinder the construc...
Vincent Yan Fu Tan, See-Kiong Ng
CVPR
2005
IEEE
14 years 9 months ago
Object Class Recognition Using Multiple Layer Boosting with Heterogeneous Features
We combine local texture features (PCA-SIFT), global features (shape context), and spatial features within a single multi-layer AdaBoost model of object class recognition. The fir...
Wei Zhang 0002, Bing Yu, Gregory J. Zelinsky, Dimi...
IIHMSP
2006
IEEE
138views Multimedia» more  IIHMSP 2006»
14 years 1 months ago
Improved Linear Prediction Technique for Musical Onset Detection
Following our previous work in [8] where a music signal is decomposed into multiple subband signals and the forward linear prediction errors of all subbands are combined to give t...
Wan-Chi Lee, C. C. Jay Kuo
CVPR
2003
IEEE
14 years 9 months ago
Kullback-Leibler Boosting
In this paper, we develop a general classification framework called Kullback-Leibler Boosting, or KLBoosting. KLBoosting has following properties. First, classification is based o...
Ce Liu, Heung-Yeung Shum
NIPS
2008
13 years 9 months ago
Multi-label Multiple Kernel Learning
We present a multi-label multiple kernel learning (MKL) formulation in which the data are embedded into a low-dimensional space directed by the instancelabel correlations encoded ...
Shuiwang Ji, Liang Sun, Rong Jin, Jieping Ye