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» Data Mining via Support Vector Machines
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ICML
2006
IEEE
16 years 5 months ago
Nonstationary kernel combination
The power and popularity of kernel methods stem in part from their ability to handle diverse forms of structured inputs, including vectors, graphs and strings. Recently, several m...
Darrin P. Lewis, Tony Jebara, William Stafford Nob...
ICML
2003
IEEE
16 years 5 months ago
Learning Metrics via Discriminant Kernels and Multidimensional Scaling: Toward Expected Euclidean Representation
Distance-based methods in machine learning and pattern recognition have to rely on a metric distance between points in the input space. Instead of specifying a metric a priori, we...
Zhihua Zhang
PKDD
2009
Springer
113views Data Mining» more  PKDD 2009»
15 years 11 months ago
Feature Selection for Density Level-Sets
A frequent problem in density level-set estimation is the choice of the right features that give rise to compact and concise representations of the observed data. We present an eï¬...
Marius Kloft, Shinichi Nakajima, Ulf Brefeld
ICDM
2003
IEEE
113views Data Mining» more  ICDM 2003»
15 years 10 months ago
Semantic Role Parsing: Adding Semantic Structure to Unstructured Text
There is a ever-growing need to add structure in the form of semantic markup to the huge amounts of unstructured text data now available. We present the technique of shallow seman...
Sameer Pradhan, Kadri Hacioglu, Wayne Ward, James ...
ICPR
2004
IEEE
16 years 5 months ago
Object Categorization via Local Kernels
In this paper we consider the problem of multi-object categorization. We present an algorithm that combines support vector machines with local features via a new class of Mercer k...
Barbara Caputo, Christian Wallraven, Maria-Elena N...