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» Algorithm Selection using Reinforcement Learning
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ICML
2004
IEEE
15 years 8 months ago
Gradient LASSO for feature selection
LASSO (Least Absolute Shrinkage and Selection Operator) is a useful tool to achieve the shrinkage and variable selection simultaneously. Since LASSO uses the L1 penalty, the optim...
Yongdai Kim, Jinseog Kim
ICANN
2009
Springer
15 years 9 months ago
Using Kernel Basis with Relevance Vector Machine for Feature Selection
This paper presents an application of multiple kernels like Kernel Basis to the Relevance Vector Machine algorithm. The framework of kernel machines has been a source of many works...
Frederic Suard, David Mercier
ICPR
2006
IEEE
16 years 4 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...
ACMICEC
2007
ACM
154views ECommerce» more  ACMICEC 2007»
15 years 7 months ago
Learning and adaptivity in interactive recommender systems
Recommender systems are intelligent E-commerce applications that assist users in a decision-making process by offering personalized product recommendations during an interaction s...
Tariq Mahmood, Francesco Ricci
MLDM
2009
Springer
15 years 9 months ago
Discretization of Target Attributes for Subgroup Discovery
Abstract. We describe an algorithm called TargetCluster for the discretization of continuous targets in subgroup discovery. The algorithm identifies patterns in the target data an...
Katherine Moreland, Klaus Truemper