Predicting the relationship between the size of training sample and the predictive power of classifiers

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

Volumn 3215, Issue , 2004, Pages 529-535

  Boonyanunta, Natthaphan ^a   Zeephongsekul, Panlop ^a  

^a RMIT UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ION BEAMS; KNOWLEDGE BASED SYSTEMS;

CLASSIFICATION TECHNIQUE; EMPIRICAL STUDIES; PREDICTIVE POWER; REAL DATA SETS; TRAINING SAMPLE;

SAMPLING;

EID: 84975489216     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-30134-9_71     Document Type: Conference Paper

References (13)

1. Freund, Y.: An adaptive version of the boosting by majority algorithm. Machine Learning 43(3) (2001) 293-318
2. Schapire, R.E.: Drifting games. Machine Learning 43(3) (2001) 265-291
3. Ueda, N.: Optimal linear combination of neural networks for improving classification performance. Proceedings of IEEE Transactions on Pattern Analysis and Machine Intelligence 22(2) (2000) 207-215
4. Webb, G.I.: MultiBoosting: a technique for combining boosting and wagging. Machine Learning 40(2) (2000) 159-196
5. Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. Proceedings of IEEE Transactions on Pattern Analysis and Machine Intelligence 20(3) (1998) 226-239
6. Weiss, S.M., Indurkhya, N.: Predictive Data Mining: A Practical Guide. Morgan Kaufmann Publishers: California (1998)
7. Witten, I.H., Frank E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann Publishers: California (2000)
8. Groth, R.: Data Mining: Building Competitive Advantage. Prentice Hall: New Jersey (2000)
9. Berry, M.J.A., Linoff G.: Data Mining Techniques For Marketing, Sales and Customer Support. John Wiley & Sons: New York (1997)
10. Lewis, E.M.: An Introduction to Credit Scoring. Athena Press: California (1992)
11. Valiant, L.G.: A theory of the learnable. Communications of the ACM 27 (1984) 1134-1142
12. Schapire, R.E.: The strength of weak learnability. Machine Learning 5 (1990) 197-227
13. Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases (1998)