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ICML
2002
IEEE
16 years 4 months ago
Is Combining Classifiers Better than Selecting the Best One
We empirically evaluate several state-of-theart methods for constructing ensembles of heterogeneous classifiers with stacking and show that they perform (at best) comparably to se...
Saso Dzeroski, Bernard Zenko
140
Voted
ICPR
2006
IEEE
16 years 5 months ago
On Kernel Selection in Relevance Vector Machines Using Stability Principle
In this paper we propose an alternative interpretation of Bayesian learning based on maximal evidence principle. We establish a notion of local evidence which can be viewed as a c...
Dmitry Kropotov, Nikita Ptashko, Oleg Vasiliev, Dm...
ICALT
2006
IEEE
15 years 10 months ago
A Shortest Learning Path Selection Algorithm in E-learning
Generally speaking, in the e-learning systems, a course is modeled as a graph, where each node represents a knowledge node (KU) and two nodes are connected to form a semantic netw...
Chengling Zhao, Liyong Wan
TNN
2010
176views Management» more  TNN 2010»
14 years 10 months ago
Sparse approximation through boosting for learning large scale kernel machines
Abstract--Recently, sparse approximation has become a preferred method for learning large scale kernel machines. This technique attempts to represent the solution with only a subse...
Ping Sun, Xin Yao
ICML
1998
IEEE
16 years 4 months ago
Feature Selection via Concave Minimization and Support Vector Machines
Computational comparison is made between two feature selection approaches for nding a separating plane that discriminates between two point sets in an n-dimensional feature space ...
Paul S. Bradley, Olvi L. Mangasarian