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Erschienen in:
2003 | OriginalPaper | Buchkapitel
Optimal neighbourhood and model quality indicators
verfasst von : Stefan Janaqi, François Hartmann, Meriam Chebre, Edith di Crescenzo
Erschienen in: Artificial Neural Nets and Genetic Algorithms
Verlag: Springer Vienna
Enthalten in: Professional Book Archive
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The construction of a good predicting model by learning algorithms does not necessarily imply a correct answer during the generalisation step. That is why one gives confidence intervals on the predicted value, often needing some hypothesis on the data’s density distribution. These hypothesis can hardly be verified when a little number of samples is given, which is the most frequent case in practice. We follow a local approach on the basis of an optimal neighbourhood choice. We use this neighbourhood to predict as well as to give some simple model quality indicators for any sample.