Bernhard Schölkopf, Sebastian Mika, Chris J. C. Burges, Philipp Knirsch, Klaus-Robert Müller, Gunnar Rätsch, Alex J. Smola  Home/Search
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Abstract: This paper collects some ideas targeted at advancing our understanding of the feature spaces associated with Support Vector (SV) kernel functions. We rst discuss the geometry of feature space. In particular, we review what is known about the shape of the image of input space under the feature space map, and how this inuences the capacity of SV methods. Following this, we describe how the metric governing the intrinsic geometry of the mapped surface can be computed in terms of the kernel, using ... (Update)
Context of citations to this paper: More
.... result in this direction is the extension of linear Principal Component Analysis (PCA) 8] to kernel PCA, shown by SchSlkopf et al. [13, 14]. The aim of this paper is to present a new simple and straightforward formulation to PCA analysis and its kernel version. The...
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BibTeX entry: (Update)
Sch61kopf B., Mika S., Burges C., Knirsch P., Miiller K.-R., R/itsch G., Smola A., "Input space vs. feature space in kernel-based methods," IEEE Transactions on Neural Networks, 10(5), 1000 1017, 1999. http://citeseer.ist.psu.edu/kopf99input.html More
@misc{ kopf99input,
author = "S. kopf and B. Mika and S. Burges and C. Knirsch and P. Miiller and K.
itsch and G. Smola",
title = "Input space vs. feature space in kernel-based methods",
text = "Sch61kopf B., Mika S., Burges C., Knirsch P., Miiller K.-R., R/itsch G.,
Smola A., Input space vs. feature space in kernel-based methods, IEEE Transactions
on Neural Networks, 10(5), 1000 1017, 1999.",
year = "1999",
url = "citeseer.ist.psu.edu/kopf99input.html" }
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