Jul 4, 2004 · We propose a novel algorithm using the framework of empirical risk minimization and marginalized kernels, and analyze its computational and ...
Apr 30, 2004 · Abstract. We consider the problem of decentralized detection under constraints on the number of bits that can be transmitted by each sensor.
Computer Science Division and Department of Statistics, U.C. Berkeley, CA 94720-1776 USA. Abstract. We consider the problem of decentralized detec-.
Bibliographic details on Decentralized detection and classification using kernel methods.
We introduce a new family of positive-definite kernel functions that mimic the computation in large, multilayer neural nets. These kernel functions can be ...
In this paper, a data-based damage detection algorithm that uses a novel one-class kernel classifier for detection and localisation of damage is presented.
[PDF] Learning in decentralized systems: A nonparametric approach
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Nonparametric decentralized detection using kernel methods. Such problems of decentralized decision-making have been the focus of considerable research in ...
& Jordan, M. I. "Decentralized detection and classification using kernel methods" International Conference on Machine Learning (ICML) , v.21 , 2004 , p.641.
Oct 11, 2017 · Abstract. We consider multi-agent stochastic optimization problems over reproducing kernel Hilbert spaces (RKHS).
This book fulfils two major roles. Firstly it provides practitioners with a large toolkit of algorithms, kernels and solutions ready to be implemented, many.
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