کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
9653561 | 679201 | 2005 | 20 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A preliminary empirical comparison of recursive neural networks and tree kernel methods on regression tasks for tree structured domains
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: A preliminary empirical comparison of recursive neural networks and tree kernel methods on regression tasks for tree structured domains A preliminary empirical comparison of recursive neural networks and tree kernel methods on regression tasks for tree structured domains](/preview/png/9653561.png)
چکیده انگلیسی
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 the feature space from structured data. We present and discuss preliminary empirical results for specific regression tasks involving well-known quantitative structure-activity and quantitative structure-property relationship (QSAR/QSPR) problems, where both the approaches are able to achieve state-of-the-art results.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 64, March 2005, Pages 73-92
Journal: Neurocomputing - Volume 64, March 2005, Pages 73-92
نویسندگان
Alessio Micheli, Filippo Portera, Alessandro Sperduti,