Adaptation to drifting concepts

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2902, Issue , 2003, Pages 279-293

  Castillo, Gladys ^a,b   Gama, João ^a   Medas, Pedro ^a  

^a UNIVERSITY OF PORTO   (Portugal)

^b UNIVERSITY OF AVEIRO   (Portugal)

Author keywords

[No Author keywords available]

Indexed keywords

CONTROL CHARTS; LEARNING SYSTEMS; REAL TIME SYSTEMS; STATISTICAL PROCESS CONTROL; SUPERVISED LEARNING; ALGORITHMS; ARTIFICIAL INTELLIGENCE;

ADAPTIVE APPROACH; CONCEPT DRIFTS; LEARNING TASKS; PERFORMANCE LEVEL; PRODUCTION PROCESS; QUALITY CHARACTERISTIC; STATISTICAL QUALITY CONTROL; TARGET CONCEPT;

LEARNING ALGORITHMS;

EID: 0348216539     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-24580-3_34     Document Type: Article

References (14)

1. Castillo, G., Gama, J., Breda, A.M.: Adaptive Bayes for a User Modeling Prediction Task based on Learning Styles. In P. Brusilovsky, A. Corbett and F. de Rosis (Eds.). User Modeling 2003. Proceedings of the Ninth International Conference. LNAI 2702, Springer-Verlag (2003) 328-332.
2. del Castillo, E.: Statistical Process Adjustment for Quality Control, John Willey and Sons, Inc., New York (2002).
3. Felder, R.M., Soloman, B.A.: Index of Learning Style Questionnaire, available online at: http://www2.ncsu.edu/unity/lockers/users/f/felder/public/ ILS-dir/ilsweb.html.
4. R.M.: Matters of Style. ASEE Prism 6 (4)(1996) 18-23.
5. Gama, J., Castillo, G.: Adaptive Bayes. Advances in Artificial Intelligence - IB-ERAMIA 2002, LNAI 2527, Springer Verlag (2002) 765-774.
6. Klinkenberg, R., Renz, I., Adaptive Information Filtering: Learning in the Presence of Concept Drifts, Learning for Text Categorization, Menlo Park, CA, USA, AAAI Press (1998) 33-40.
7. Klinkenberg, R., Joachims, T.: Detecting Concept Drift with Support Vector Machines, Proceedings of the Seventeenth International Conference on Machine Learning (ICML), San Franciso, Morgan Kaufman (2000).
8. Lanquillon, C.: Enhancing Test Classification to Improve Information Filtering, PhD. Dissertation (2001), University of Madgdeburg, Germany, available on-line at: http://diglib.uni-magdeburg.de/Dissertationen/2001/carlanquillon.pdf
9. Maloof, M., Michalski, R.: Selecting Examples for Partial Memory Learning, Machine Learning 41 (2000) 27-52.
10. Medas, P., Gama, J.: Aprendizagem com Detecção de Mudança de Conceito, Actas das X Jornadas de Classificação e Análise de Dados, Aveiro (2003) 27-31.
11. Mitchell, Tom. Machine Learning. McGraw Hill, (1997).
12. Montgomery, D.C.: Introduction to Statistical Quality Control (3rd ed.), John Willey & Sons, Inc., New York (1997).
13. Webb, G., Pazzani, M., Billsus, D.: Machine Learning for User Modeling. In User Modeling and User-Adapted Interaction 11 (2001) 19-29.
14. Widmer, G., Kubat, M.: Learning in the Presence of Concept Drift and Hidden Context. Machine Learning 23 (1996) 69-101.