The nature of statistical learning theory
The nature of statistical learning theory
Characterization of the Sonar Signals Benchmark
Neural Processing Letters
Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Model Selection and Error Estimation
Machine Learning
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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 counterbased digital hardware. Experiments on two real world data sets show the validity of the proposed method.