Improving Web Learning through model Optimization using Bootstrap for a Tour-Guide Robot

Improving Web Learning through model Optimization using Bootstrap for a Tour-Guide Robot

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 Class...

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Bibliographic Details
Main Authors: Rafael León, J. Javier Rainer, José Manuel Rojo, Ramón Galán
Format: Article
Language:English
Published: Universidad Internacional de La Rioja (UNIR) 2012-09-01
Series:International Journal of Interactive Multimedia and Artificial Intelligence
Subjects:
Web Mining
Supervised Learning
Bootstrap
Patterns Mining
Web Classifiers
Knowledge Management
Online Access:http://www.ijimai.org/journal/sites/default/files/IJIMAI20121_6_2.pdf
Description
Summary: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
ISSN:1989-1660

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