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 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 standard SVR formulation for real-valued function approximation on multi-dimensional 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)
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