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» On Relevant Dimensions in Kernel Feature Spaces
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AAAI
2006
13 years 9 months ago
kFOIL: Learning Simple Relational Kernels
A novel and simple combination of inductive logic programming with kernel methods is presented. The kFOIL algorithm integrates the well-known inductive logic programming system FO...
Niels Landwehr, Andrea Passerini, Luc De Raedt, Pa...
NIPS
2004
13 years 8 months ago
Efficient Kernel Discriminant Analysis via QR Decomposition
Linear Discriminant Analysis (LDA) is a well-known method for feature extraction and dimension reduction. It has been used widely in many applications such as face recognition. Re...
Tao Xiong, Jieping Ye, Qi Li, Ravi Janardan, Vladi...
SPLC
2008
13 years 9 months ago
Modeling the Variability Space of Self-Adaptive Applications
Modeling self-adaptive applications is a difficult task due to the complex relationships they have with their environments. Designers of such applications strive to model accurate...
Gilles Perrouin, Franck Chauvel, Julien DeAntoni, ...
COSIT
1999
Springer
145views GIS» more  COSIT 1999»
13 years 11 months ago
The Nature of Landmarks for Real and Electronic Spaces
Landmarks are significant in one’s formation of a cognitive map of both physical environments and electronic information spaces. Landmarks are defined in physical space as having...
Molly E. Sorrows, Stephen C. Hirtle
DIS
2007
Springer
14 years 1 months ago
A Hilbert Space Embedding for Distributions
We describe a technique for comparing distributions without the need for density estimation as an intermediate step. Our approach relies on mapping the distributions into a reprodu...
Alexander J. Smola, Arthur Gretton, Le Song, Bernh...