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106
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CCE
2004
15 years 2 months ago
On the use of reduced order models in bifurcation analysis of distributed parameter systems
Bifurcation theory provides a powerful tool for analyzing the nonlinear dynamic behavior of process systems. However, although the theory in principle applies to lumped as well as...
Yi Liu, Elling W. Jacobsen
113
Voted
DCC
2008
IEEE
16 years 2 months ago
Re-pair Achieves High-Order Entropy
Re-Pair is a dictionary-based compression method invented in 1999 by Larsson and Moffat. Although its practical performance has been established through experiments, the method ha...
Gonzalo Navarro, Luís M. S. Russo
107
Voted
ICPR
2010
IEEE
15 years 7 months ago
Gaussian Process Learning from Order Relationships Using Expectation Propagation
A method for Gaussian process learning of a scalar function from a set of pair-wise order relationships is presented. Expectation propagation is used to obtain an approximation to...
Ruixuan Wang, Stephen James Mckenna
ECML
2004
Springer
15 years 6 months ago
Efficient Hyperkernel Learning Using Second-Order Cone Programming
The kernel function plays a central role in kernel methods. Most existing methods can only adapt the kernel parameters or the kernel matrix based on empirical data. Recently, Ong e...
Ivor W. Tsang, James T. Kwok
112
Voted
AAAI
2006
15 years 4 months ago
Unsupervised Order-Preserving Regression Kernel for Sequence Analysis
In this work, a generalized method for learning from sequence of unlabelled data points based on unsupervised order-preserving regression is proposed. Sequence learning is a funda...
Young-In Shin