#### Filter Results:

- Full text PDF available (7)

#### Publication Year

2003

2005

- This year (0)
- Last 5 years (0)
- Last 10 years (0)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Key Phrases

Learn More

- Alessio Micheli, Filippo Portera, Alessandro Sperduti
- Neurocomputing
- 2005

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)

- Alessio Micheli, Filippo Portera, Alessandro Sperduti
- ESANN
- 2004

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.

- Filippo Portera, Alessandro Sperduti
- ECAI
- 2004

The standard SVM formulation for binary classification is based on the Hinge loss function, where errors are considered not correlated. Due to this, local information in the feature space which can be useful to improve the prediction model is disregarded. In this paper we address this problem by defining a generalized quadratic loss where the co-occurrence… (More)

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)

- Filippo Portera, Alessandro Sperduti
- WIRN
- 2004

The standard SVR formulation for real-valued function approximation on multidimensional spaces is based on the -insensitive loss function, where errors are considered not correlated. Due to this, local information in the feature space which can be useful to improve the prediction model is disregarded. In this paper we address this problem by defining a… (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)

- ‹
- 1
- ›