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» Kernels for Semi-Structured Data
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119
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ESANN
2003
15 years 4 months ago
Approximately unbiased estimation of conditional variance in heteroscedastic kernel ridge regression
In this paper we extend a form of kernel ridge regression for data characterised by a heteroscedastic noise process (introduced in Foxall et al. [1]) in order to provide approxima...
Gavin C. Cawley, Nicola L. C. Talbot, Robert J. Fo...
148
Voted
ICIP
2010
IEEE
15 years 1 months ago
Combining free energy score spaces with information theoretic kernels: Application to scene classification
Most approaches to learn classifiers for structured objects (e.g., images) use generative models in a classical Bayesian framework. However, state-of-the-art classifiers for vecto...
Manuele Bicego, Alessandro Perina, Vittorio Murino...
116
Voted
SDM
2009
SIAM
172views Data Mining» more  SDM 2009»
16 years 17 days ago
Travel-Time Prediction Using Gaussian Process Regression: A Trajectory-Based Approach.
This paper is concerned with the task of travel-time prediction for an arbitrary origin-destination pair on a map. Unlike most of the existing studies, which focus only on a parti...
Sei Kato, Tsuyoshi Idé
122
Voted
FDL
2006
IEEE
15 years 9 months ago
Design Structure Analysis and Transaction Recording in SystemC
We present an introspection/reflection framework for SystemC which extracts design-relevant structure information and transaction data under any LRM-2.1 compliant simulation kern...
Wolfgang Klingauf, Manuel Geffken
NIPS
2004
15 years 4 months ago
An Application of Boosting to Graph Classification
This paper presents an application of Boosting for classifying labeled graphs, general structures for modeling a number of real-world data, such as chemical compounds, natural lan...
Taku Kudo, Eisaku Maeda, Yuji Matsumoto