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» A PAC Bound for Approximate Support Vector Machines
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CVPR
2008
IEEE
14 years 9 months ago
Margin-based discriminant dimensionality reduction for visual recognition
Nearest neighbour classifiers and related kernel methods often perform poorly in high dimensional problems because it is infeasible to include enough training samples to cover the...
Hakan Cevikalp, Bill Triggs, Frédéri...
NN
2002
Springer
115views Neural Networks» more  NN 2002»
13 years 7 months ago
A self-organising network that grows when required
The ability to grow extra nodes is a potentially useful facility for a self-organising neural network. A network that can add nodes into its map space can approximate the input sp...
Stephen Marsland, Jonathan Shapiro, Ulrich Nehmzow
ASM
2004
ASM
14 years 22 days ago
Observations on the Decidability of Transitions
Consider a multiple-agent transition system such that, for some basic types T1, . . . , Tn, the state of any agent can be represented as an element of the Cartesian product T1 ×·...
Yuri Gurevich, Rostislav Yavorskiy
KDD
2008
ACM
178views Data Mining» more  KDD 2008»
14 years 7 months ago
Training structural svms with kernels using sampled cuts
Discriminative training for structured outputs has found increasing applications in areas such as natural language processing, bioinformatics, information retrieval, and computer ...
Chun-Nam John Yu, Thorsten Joachims
ICASSP
2011
IEEE
12 years 11 months ago
Using the kernel trick in compressive sensing: Accurate signal recovery from fewer measurements
Compressive sensing accurately reconstructs a signal that is sparse in some basis from measurements, generally consisting of the signal’s inner products with Gaussian random vec...
Hanchao Qi, Shannon Hughes