Adaptive Model Selection for Digital Linear Classifiers

Boni, Andrea (2002) Adaptive Model Selection for Digital Linear Classifiers. UNSPECIFIED. (In Press)

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    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

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