Boni, Andrea (2002) Adaptive Model Selection for Digital Linear Classifiers. UNSPECIFIED. (In Press)
![[img]](http://eprints.biblio.unitn.it/style/images/fileicons/application_pdf.png)
| PDF Download (211Kb) | Preview |
Abstract
Adaptive model selection can be defined as the process thanks to which an optimal classifiers h* is automatically selected from a function class H by using only a given set of examples z. Such a process is particularly critic when the number of examples in z is low, because it is impossible the classical splitting of z in training + test + validation. In this work we show that the joined investigation of two bounds of the prediction error of the classifier can be useful to select h* by using z for both model selection and training. Our learning algorithm is a simple kernel-based Perceptron that can be easily implemented in a counter-based digital hardware. Experiments on two real world data sets show the validity of the proposed method.
| Item Type: | Departmental Technical Report |
|---|---|
| Department or Research center: | Information Engineering and Computer Science |
| Subjects: | Q Science > QA Mathematics > QA075 Electronic computers. Computer science T Technology > TA Engineering (General). Civil engineering (General) > TA174 Engineering Design |
| Uncontrolled Keywords: | Digital Electronics, Neural Networks, Learning Theory |
| Additional Information: | To be published in Proceedings International Conference on Artificial Neural Networks 2002 |
| Report Number: | DIT-02-034 |
| Repository staff approval on: | 21 Jan 2003 |
Actions (login required)
| View Item |
