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» Kernels for Semi-Structured Data
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136
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SLSFS
2005
Springer
15 years 8 months ago
Random Projection, Margins, Kernels, and Feature-Selection
Random projection is a simple technique that has had a number of applications in algorithm design. In the context of machine learning, it can provide insight into questions such as...
Avrim Blum
96
Voted
ESANN
2007
15 years 4 months ago
Clustering a medieval social network by SOM using a kernel based distance measure
Abstract. In order to explore the social organization of a medieval peasant community before the Hundred Years’ War, we propose the use of an adaptation of the well-known Kohonen...
Nathalie Villa, Romain Boulet
142
Voted
KDD
2009
ACM
230views Data Mining» more  KDD 2009»
16 years 3 months ago
Cross domain distribution adaptation via kernel mapping
When labeled examples are limited and difficult to obtain, transfer learning employs knowledge from a source domain to improve learning accuracy in the target domain. However, the...
ErHeng Zhong, Wei Fan, Jing Peng, Kun Zhang, Jiang...
134
Voted
ESANN
2006
15 years 4 months ago
Variants of Unsupervised Kernel Regression: General cost functions
We present an extension to a recent method for learning of nonlinear manifolds, which allows to incorporate general cost functions. We focus on the -insensitive loss and visually d...
Stefan Klanke, Helge Ritter
125
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ICML
2004
IEEE
16 years 3 months ago
Robust feature induction for support vector machines
The goal of feature induction is to automatically create nonlinear combinations of existing features as additional input features to improve classification accuracy. Typically, no...
Rong Jin, Huan Liu