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COLT
2007
Springer
15 years 4 months ago
How Good Is a Kernel When Used as a Similarity Measure?
Recently, Balcan and Blum [1] suggested a theory of learning based on general similarity functions, instead of positive semi-definite kernels. We study the gap between the learnin...
Nathan Srebro
ICML
2006
IEEE
15 years 10 months ago
On a theory of learning with similarity functions
Kernel functions have become an extremely popular tool in machine learning, with an attractive theory as well. This theory views a kernel as implicitly mapping data points into a ...
Maria-Florina Balcan, Avrim Blum
JMLR
2007
104views more  JMLR 2007»
14 years 9 months ago
Learnability of Gaussians with Flexible Variances
Gaussian kernels with flexible variances provide a rich family of Mercer kernels for learning algorithms. We show that the union of the unit balls of reproducing kernel Hilbert s...
Yiming Ying, Ding-Xuan Zhou
SSPR
2004
Springer
15 years 3 months ago
Kernel Methods for Exploratory Pattern Analysis: A Demonstration on Text Data
Kernel Methods are a class of algorithms for pattern analysis with a number of convenient features. They can deal in a uniform way with a multitude of data types and can be used to...
Tijl De Bie, Nello Cristianini
KES
2006
Springer
14 years 9 months ago
Sensor Network Localization Using Least Squares Kernel Regression
Abstract. This paper considers the sensor network localization problem using signal strength. Unlike range-based methods signal strength information is stored in a kernel matrix. L...
Anthony Kuh, Chaopin Zhu, Danilo P. Mandic