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» On High Dimensional Skylines
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GECCO
2007
Springer
192views Optimization» more  GECCO 2007»
15 years 7 months ago
SDR: a better trigger for adaptive variance scaling in normal EDAs
Recently, advances have been made in continuous, normal– distribution–based Estimation–of–Distribution Algorithms (EDAs) by scaling the variance up from the maximum–like...
Peter A. N. Bosman, Jörn Grahl, Franz Rothlau...
SAC
2006
ACM
15 years 6 months ago
The impact of sample reduction on PCA-based feature extraction for supervised learning
“The curse of dimensionality” is pertinent to many learning algorithms, and it denotes the drastic raise of computational complexity and classification error in high dimension...
Mykola Pechenizkiy, Seppo Puuronen, Alexey Tsymbal
107
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MM
2005
ACM
122views Multimedia» more  MM 2005»
15 years 6 months ago
Image clustering with tensor representation
We consider the problem of image representation and clustering. Traditionally, an n1 × n2 image is represented by a vector in the Euclidean space Rn1×n2 . Some learning algorith...
Xiaofei He, Deng Cai, Haifeng Liu, Jiawei Han
GECCO
2005
Springer
102views Optimization» more  GECCO 2005»
15 years 6 months ago
Latent variable crossover for k-tablet structures and its application to lens design problems
This paper presents the Real-coded Genetic Algorithms for high-dimensional ill-scaled structures, what is called, the ktablet structure. The k-tablet structure is the landscape th...
Jun Sakuma, Shigenobu Kobayashi
MM
2004
ACM
167views Multimedia» more  MM 2004»
15 years 6 months ago
Learning an image manifold for retrieval
We consider the problem of learning a mapping function from low-level feature space to high-level semantic space. Under the assumption that the data lie on a submanifold embedded ...
Xiaofei He, Wei-Ying Ma, HongJiang Zhang