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AGI
2008
15 years 3 months ago
Vector Symbolic Architectures: A New Building Material for Artificial General Intelligence
We provide an overview of Vector Symbolic Architectures (VSA), a class of structured associative memory models that offers a number of desirable features for artificial general int...
Simon D. Levy, Ross Gayler
PRL
2008
356views more  PRL 2008»
15 years 1 months ago
Improving image segmentation by gradient vector flow and mean shift
The classical gradient vector flow (GVF) method suffers from deficiency in the presence of other significant edges adjacent to the real boundary. In this paper, we propose an impr...
Tangwei Liu, Huiyu Zhou, Faquan Lin, Yusheng Pang,...
TIT
2008
81views more  TIT 2008»
15 years 1 months ago
Lagrangian Vector Quantization With Combined Entropy and Codebook Size Constraints
Abstract--In this paper, the Lagrangian formulation of variablerate vector quantization is extended to quantization with simultaneous constraints on entropy and codebook size, incl...
Robert M. Gray, Tamás Linder, John T. Gill ...
JMLR
2007
104views more  JMLR 2007»
15 years 1 months ago
Comments on the "Core Vector Machines: Fast SVM Training on Very Large Data Sets"
In a recently published paper in JMLR, Tsang et al. (2005) present an algorithm for SVM called Core Vector Machines (CVM) and illustrate its performances through comparisons with ...
Gaëlle Loosli, Stéphane Canu
ML
2002
ACM
121views Machine Learning» more  ML 2002»
15 years 1 months ago
Choosing Multiple Parameters for Support Vector Machines
The problem of automatically tuning multiple parameters for pattern recognition Support Vector Machines (SVMs) is considered. This is done by minimizing some estimates of the gener...
Olivier Chapelle, Vladimir Vapnik, Olivier Bousque...