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ICPR
2010
IEEE
13 years 8 months ago
Localized Multiple Kernel Regression
Multiple kernel learning (MKL) uses a weighted combination of kernels where the weight of each kernel is optimized during training. However, MKL assigns the same weight to a kerne...
Mehmet Gönen, Ethem Alpaydin
COMBINATORICS
2006
144views more  COMBINATORICS 2006»
13 years 5 months ago
Weighted Zeta Functions of Graph Coverings
We present a decomposition formula for the weighted zeta function of an irregular covering of a graph by its weighted L-functions. Moreover, we give a factorization of the weighte...
Iwao Sato
NIPS
2004
13 years 6 months ago
Nonparametric Transforms of Graph Kernels for Semi-Supervised Learning
We present an algorithm based on convex optimization for constructing kernels for semi-supervised learning. The kernel matrices are derived from the spectral decomposition of grap...
Xiaojin Zhu, Jaz S. Kandola, Zoubin Ghahramani, Jo...
GECCO
2005
Springer
195views Optimization» more  GECCO 2005»
13 years 10 months ago
Evolutionary strategies for multi-scale radial basis function kernels in support vector machines
In support vector machines (SVM), the kernel functions which compute dot product in feature space significantly affect the performance of classifiers. Each kernel function is suit...
Tanasanee Phienthrakul, Boonserm Kijsirikul
ASPDAC
2005
ACM
120views Hardware» more  ASPDAC 2005»
13 years 7 months ago
STACCATO: disjoint support decompositions from BDDs through symbolic kernels
Abstract— A disjoint support decomposition (DSD) is a representation of a Boolean function F obtained by composing two or more simpler component functions such that the component...
Stephen Plaza, Valeria Bertacco