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ICML
2000
IEEE
16 years 2 months ago
Learning Subjective Functions with Large Margins
In manyoptimization and decision problems the objective function can be expressed as a linear combinationof competingcriteria, the weights of whichspecify the relative importanceo...
Claude-Nicolas Fiechter, Seth Rogers
IWANN
2009
Springer
15 years 8 months ago
Optimising Machine-Learning-Based Fault Prediction in Foundry Production
Abstract. Microshrinkages are known as probably the most difficult defects to avoid in high-precision foundry. The presence of this failure renders the casting invalid, with the su...
Igor Santos, Javier Nieves, Yoseba K. Penya, Pablo...
ECAL
2001
Springer
15 years 6 months ago
Pareto Optimality in Coevolutionary Learning
We develop a novel coevolutionary algorithm based upon the concept of Pareto optimality. The Pareto criterion is core to conventional multi-objective optimization (MOO) algorithms....
Sevan G. Ficici, Jordan B. Pollack
NN
1998
Springer
15 years 1 months ago
A tennis serve and upswing learning robot based on bi-directional theory
We experimented on task-level robot learning based on bi-directional theory. The via-point representation was used for ‘learning by watching’. In our previous work, we had a r...
Hiroyuki Miyamoto, Mitsuo Kawato
CVPR
2008
IEEE
16 years 3 months ago
Visual tracking via incremental Log-Euclidean Riemannian subspace learning
Recently, a novel Log-Euclidean Riemannian metric [28] is proposed for statistics on symmetric positive definite (SPD) matrices. Under this metric, distances and Riemannian means ...
Xi Li, Weiming Hu, Zhongfei Zhang, Xiaoqin Zhang, ...