Improving Web learning through model optimization using bootstrap for a tour-guide robot

1. León Sanz, Rafael
2. Rainer Granados, José Javier
3. Rojo, José Manuel
4. Galán López, Ramón

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DOI: 10.9781/IJIMAI.2012.162 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

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We perform a review of Web Mining techniques and we describe a Bootstrap Statistics methodology applied to pattern model classifier optimization and verification for Supervised Learning for Tour-Guide Robot knowledge repository management. It is virtually impossible to test thoroughly Web Page Classifiers and many other Internet Applications with pure empirical data, due to the need for human intervention to generate training sets and test sets. We propose using the computer-based Bootstrap paradigm to design a test environment where they are checked with better reliability.

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