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» Generalization Bounds for Learning Kernels
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ICCV
2005
IEEE
14 years 11 months ago
A Supervised Learning Framework for Generic Object Detection in Images
In recent years Kernel Principal Component Analysis (Kernel PCA) has gained much attention because of its ability to capture nonlinear image features, which are particularly impor...
Saad Ali, Mubarak Shah
CIE
2007
Springer
14 years 4 months ago
The Complexity Ecology of Parameters: An Illustration Using Bounded Max Leaf Number
In the framework of parameterized complexity, exploring how one parameter affects the complexity of a different parameterized (or unparameterized problem) is of general interest....
Michael R. Fellows, Frances A. Rosamond
TIP
2008
175views more  TIP 2008»
13 years 9 months ago
Customizing Kernel Functions for SVM-Based Hyperspectral Image Classification
Previous research applying kernel methods such as support vector machines (SVMs) to hyperspectral image classification has achieved performance competitive with the best available ...
Baofeng Guo, Steve R. Gunn, Robert I. Damper, Jame...
PKDD
2009
Springer
103views Data Mining» more  PKDD 2009»
14 years 4 months ago
Kernels for Periodic Time Series Arising in Astronomy
Abstract. We present a method for applying machine learning algorithms to the automatic classification of astronomy star surveys using time series of star brightness. Currently su...
Gabriel Wachman, Roni Khardon, Pavlos Protopapas, ...
COLT
2005
Springer
14 years 3 months ago
Rank, Trace-Norm and Max-Norm
We study the rank, trace-norm and max-norm as complexity measures of matrices, focusing on the problem of fitting a matrix with matrices having low complexity. We present generali...
Nathan Srebro, Adi Shraibman