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» Optimizing Sorting with Machine Learning Algorithms
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ICML
2004
IEEE
15 years 7 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
117
Voted
EDUTAINMENT
2006
Springer
15 years 5 months ago
Research of Dynamic Terrain in Complex Battlefield Environments
In this paper, we present a novel method for dynamic terrain in battlefield and an efficient plan to simulate crater in the battle. We explore a few methods for dynamic terrain sur...
Xingquan Cai, Fengxia Li, Haiyan Sun, Shouyi Zhan
119
Voted
BMCBI
2010
97views more  BMCBI 2010»
15 years 2 months ago
Biomarker discovery in heterogeneous tissue samples -taking the in-silico deconfounding approach
Background: For heterogeneous tissues, such as blood, measurements of gene expression are confounded by relative proportions of cell types involved. Conclusions have to rely on es...
Dirk Repsilber, Sabine Kern, Anna Telaar, Gerhard ...
GECCO
2005
Springer
129views Optimization» more  GECCO 2005»
15 years 7 months ago
Evolutionary change in developmental timing
This paper presents a mutation-based evolutionary algorithm that evolves genotypic genes for regulating developmental timing of phenotypic values. The genotype sequentially genera...
Kei Ohnishi, Kaori Yoshida
ICML
2003
IEEE
16 years 3 months ago
Q-Decomposition for Reinforcement Learning Agents
The paper explores a very simple agent design method called Q-decomposition, wherein a complex agent is built from simpler subagents. Each subagent has its own reward function and...
Stuart J. Russell, Andrew Zimdars