The nature of statistical learning theory
The nature of statistical learning theory
Separability Index in Supervised Learning
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Efficient svm training using low-rank kernel representations
The Journal of Machine Learning Research
Invariance of neighborhood relation under input space to feature space mapping
Pattern Recognition Letters
Learning the Kernel with Hyperkernels
The Journal of Machine Learning Research
An efficient kernel matrix evaluation measure
Pattern Recognition
An effective method for locally neighborhood graphs updating
DEXA'05 Proceedings of the 16th international conference on Database and Expert Systems Applications
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We extend the framework of spatial autocorrelation analysis on Reproducing Kernel Hilbert Space (RKHS). Our results are based on the fact that some geometrical neighborhood structures vary when samples are mapped into a RKHS, while other neighborhood structures do not. These results allow us to design a new measure for measuring the goodness of a kernel and more generally a similarity matrix. Experiments on UCI datasets show the relevance of our methodology.