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» Kernels for Semi-Structured Data
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ICASSP
2009
IEEE
14 years 4 months ago
Combining VTS model compensation and support vector machines
It is difficult to adapt discriminative classifiers, particularly kernel based ones such as support vector machines (SVMs), to handle mismatches between the training and test da...
Mark J. F. Gales, Federico Flego
CDC
2009
IEEE
221views Control Systems» more  CDC 2009»
14 years 2 months ago
Parametrization invariant covariance quantification in identification of transfer functions for linear systems
This paper adresses the variance quantification problem for system identification based on the prediction error framework. The role of input and model class selection for the auto-...
Tzvetan Ivanov, Michel Gevers
AUSDM
2008
Springer
199views Data Mining» more  AUSDM 2008»
14 years 1 days ago
Kernel-based Visualisation of Genes with the Gene Ontology
With the development of microarray
Hamid Ghous, Paul J. Kennedy, Daniel R. Catchpoole...
PR
2010
156views more  PR 2010»
13 years 8 months ago
Semi-supervised clustering with metric learning: An adaptive kernel method
Most existing representative works in semi-supervised clustering do not sufficiently solve the violation problem of pairwise constraints. On the other hand, traditional kernel met...
Xuesong Yin, Songcan Chen, Enliang Hu, Daoqiang Zh...
DAGM
2011
Springer
12 years 9 months ago
Relaxed Exponential Kernels for Unsupervised Learning
Many unsupervised learning algorithms make use of kernels that rely on the Euclidean distance between two samples. However, the Euclidean distance is optimal for Gaussian distribut...
Karim T. Abou-Moustafa, Mohak Shah, Fernando De la...