Filippo Portera

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The aim of this paper is to start a comparison between Recursive Neural Networks (RecNN) and kernel methods for structured data, specifically Support Vector Regression (SVR) machine using a Tree Kernel, in the context of regression tasks for trees. Both the approaches can deal directly with a structured input representation and differ in the construction of(More)
We consider two different methods for QSAR/QSPR regression tasks: Recursive Neural Networks (RecNN) and a Support Vector Regression (SVR) machine using a Tree Kernel. Experimental results on two specific regression tasks involving alkanes and benzodiazepines are obtained for the two approaches.
The classical SVM approach to solve multilabel problems consists in training a single classifier for each class. We propose a compact model that considers the whole set of classifiers at once. Our strategy focuses on the shared use of the kernel matrix information between different classifiers in order to reduce the complexity of the learning task.(More)
  • Caroline K. Stahlet, Regis P. Brekosky, +7 authors aNASA Goddard
  • 2003
X-ray microcalorimeters using transition-edge sensors (TES) show great promise for use in astronomical x-ray spectroscopy. We have obtained very high energy resolution (2.8 eV at 1.5 keV and 3.7 eV at 3.3 keV) in a large, isolated TES pixel using a Mo/Au proximity-effect bilayer on a silicon nitride membrane. We will discuss the performance and our(More)
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