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» Online Learning of Noisy Data with Kernels
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ICPR
2008
IEEE
14 years 2 months ago
Online anomal movement detection based on unsupervised incremental learning
We propose an online anomal movement detection method using incremental unsupervised learning. As the feature for discrimination, we extract the principal component of the spatio-...
Kyoko Sudo, Tatsuya Osawa, Hidenori Tanaka, Hideki...
ICANN
2001
Springer
14 years 4 days ago
Incremental Support Vector Machine Learning: A Local Approach
Abstract. In this paper, we propose and study a new on-line algorithm for learning a SVM based on Radial Basis Function Kernel: Local Incremental Learning of SVM or LISVM. Our meth...
Liva Ralaivola, Florence d'Alché-Buc
CVPR
2009
IEEE
15 years 2 months ago
Shared Kernel Information Embedding for Discriminative Inference
Latent Variable Models (LVM), like the Shared-GPLVM and the Spectral Latent Variable Model, help mitigate over- fitting when learning discriminative methods from small or modera...
David J. Fleet, Leonid Sigal, Roland Memisevic
ICASSP
2010
IEEE
13 years 7 months ago
Fixed-budget kernel recursive least-squares
We present a kernel-based recursive least-squares (KRLS) algorithm on a fixed memory budget, capable of recursively learning a nonlinear mapping and tracking changes over time. I...
Steven Van Vaerenbergh, Ignacio Santamaría,...
ICML
2006
IEEE
14 years 8 months ago
Kernel Predictive Linear Gaussian models for nonlinear stochastic dynamical systems
The recent Predictive Linear Gaussian model (or PLG) improves upon traditional linear dynamical system models by using a predictive representation of state, which makes consistent...
David Wingate, Satinder P. Singh