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CDC
2008
IEEE
129views Control Systems» more  CDC 2008»
14 years 11 months ago
Using polynomial semi-separable kernels to construct infinite-dimensional Lyapunov functions
Abstract-- In this paper, we introduce the class of semiseparable kernel functions for use in constructing Lyapunov functions for distributed-parameter systems such as delaydiffere...
Matthew M. Peet, Antonis Papachristodoulou
DCC
2005
IEEE
15 years 9 months ago
Using 2: 1 Shannon Mapping for Joint Source-Channel Coding
Fredrik Hekland, Geir E. Øien, Tor A. Ramst...
NIPS
2007
14 years 11 months ago
Random Features for Large-Scale Kernel Machines
To accelerate the training of kernel machines, we propose to map the input data to a randomized low-dimensional feature space and then apply existing fast linear methods. The feat...
Ali Rahimi, Benjamin Recht
TIP
2011
162views more  TIP 2011»
14 years 4 months ago
Kernel Maximum Autocorrelation Factor and Minimum Noise Fraction Transformations
—This paper introduces kernel versions of maximum autocorrelation factor (MAF) analysis and minimum noise fraction (MNF) analysis. The kernel versions are based upon a dual formu...
Allan Aasbjerg Nielsen
62
Voted
IJON
2006
76views more  IJON 2006»
14 years 9 months ago
Joint maps for orientation, eye, and direction preference in a self-organizing model of V1
Primary visual cortex (V1) contains overlaid feature maps for orientation (OR), motion direction selectivity (DR), and ocular dominance (OD). Neurons in these maps are connected l...
James A. Bednar, Risto Miikkulainen