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CSB
2005
IEEE
115views Bioinformatics» more  CSB 2005»
13 years 10 months ago
A New Clustering Strategy with Stochastic Merging and Removing Based on Kernel Functions
With hierarchical clustering methods, divisions or fusions, once made, are irrevocable. As a result, when two elements in a bottom-up algorithm are assigned to one cluster, they c...
Huimin Geng, Hesham H. Ali
CORR
2012
Springer
214views Education» more  CORR 2012»
12 years 17 days ago
Stochastic Low-Rank Kernel Learning for Regression
We present a novel approach to learn a kernelbased regression function. It is based on the use of conical combinations of data-based parameterized kernels and on a new stochastic ...
Pierre Machart, Thomas Peel, Liva Ralaivola, Sandr...
ALT
2010
Springer
13 years 6 months ago
Online Multiple Kernel Learning: Algorithms and Mistake Bounds
Online learning and kernel learning are two active research topics in machine learning. Although each of them has been studied extensively, there is a limited effort in addressing ...
Rong Jin, Steven C. H. Hoi, Tianbao Yang
SODA
2012
ACM
177views Algorithms» more  SODA 2012»
11 years 7 months ago
Stochastic coalescence in logarithmic time
The following distributed coalescence protocol was introduced by Dahlia Malkhi in 2006 motivated by applications in social networking. Initially there are n agents wishing to coal...
Po-Shen Loh, Eyal Lubetzky
ICMCS
2006
IEEE
155views Multimedia» more  ICMCS 2006»
13 years 11 months ago
Region-Based Image Retrieval using Radial Basis Function Network
This paper presents a new framework that integrates relevance feedback into region-based image retrieval (RBIR) systems based on radial basis function network (RBFN). A modified u...
Kui Wu, Kim-Hui Yap, Lap-Pui Chau