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» Boosting Methods for Regression
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ICPR
2008
IEEE
14 years 11 months ago
Collaborative learning by boosting in distributed environments
In this paper we propose a new distributed learning method called distributed network boosting (DNB) algorithm for distributed applications. The learned hypotheses are exchanged b...
Shijun Wang, Changshui Zhang
ECCV
2010
Springer
14 years 3 months ago
Robust Multi-View Boosting with Priors
Many learning tasks for computer vision problems can be described by multiple views or multiple features. These views can be exploited in order to learn from unlabeled data, a.k.a....
CSDA
2006
87views more  CSDA 2006»
13 years 9 months ago
Choice of B-splines with free parameters in the flexible discriminant analysis context
Flexible discriminant analysis (FDA) is a general methodology which aims at providing tools for multigroup non linear classification. It consists in a nonparametric version of dis...
Christelle Reynès, Robert Sabatier, Nicolas...
KDD
2010
ACM
275views Data Mining» more  KDD 2010»
14 years 1 months ago
Combined regression and ranking
Many real-world data mining tasks require the achievement of two distinct goals when applied to unseen data: first, to induce an accurate preference ranking, and second to give g...
D. Sculley
TSMC
2008
99views more  TSMC 2008»
13 years 9 months ago
Robust Regularized Kernel Regression
Robust regression techniques are critical to fitting data with noise in real-world applications. Most previous work of robust kernel regression is usually formulated into a dual fo...
Jianke Zhu, Steven C. H. Hoi, Michael R. Lyu