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CSB
2005
IEEE
115views Bioinformatics» more  CSB 2005»
14 years 2 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 4 months 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 10 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 10 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»
14 years 2 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