Combining Classifiers in Error Correcting Output Coding

Systems and Computers in Japan

Volumn 35, Issue 4, 2004, Pages 9-18

  Yamaguchi, Nobuhiko ^a   Ishii, Naohiro ^a  

^a NAGOYA INSTITUTE OF TECHNOLOGY   (Japan)

Author keywords

Artificial neural network; Ensemble learning; Error correction coding; Pattern recognition

Indexed keywords

CLASSIFIERS; DECODING; NEURAL NETWORKS; PATTERN RECOGNITION; PROBABILITY;

ENSEMBLE LEARNING; ERROR CORRECTION CODES;

ERROR CORRECTION;

EID: 16544362281     PISSN: 08821666     EISSN: None     Source Type: Journal    
DOI: 10.1002/scj.10549     Document Type: Article

References (21)

1. Aha D, Bankert R. Cloud classification using error-correcting output codes. Artificial Intelligence Applications: Natural Resources, Agriculture, and Environmental Science 1997;11:13-28.
2. Bakiri G, Dietterich T. Achieving high-accuracy text-to-speech with machine learning. Data Mining and Speech Synthesis, 1999.
3. Berger A. Error correcting output coding for text classification. IJCAI '99: Workshop on Machine Learning for Information Filtering.
4. Breiman L. Bagging estimators. Machine Learning 1996;24:123-140.
5. Crammer K. On the learnability and design of output codes for multiclass problems. Machine Learning 2002;47:201-233.
6. Dietterich T. Machine learning research: Four current directions. AI Mag 1998;18:97-136.
7. Dietterich T, Bakiri G. Solving multiclass learning problems via error-correcting output codes. J Artif Intell Res 1995;2:263-286.
8. Duda R, Hart P, Stork D. Pattern classification (2nd ed.). Wiley-Interscience; 2000.
9. Ghaderi R, Windeatt T. Least squares and estimation measures via error correcting output code. Second International Workshop Multiple Classifier Systems, p 148-157, Cambridge, UK, 2001.
10. James G. Majority vole classifiers: theory and applications. Ph.D. thesis, Stanford University, 1998.
11. James G, Hastie T. The error coding method and PICTs. J Comput Graph Statistics 1998;7:377-387.
12. Kittler J, Roli F (editors). Multiple Classifier Systems: Proc Second International Workshop, MCS 2001. Lecture Notes in Computer Science 2096. Springer-Verlag; 2001.
13. Kong E, Dietterich T. Error-correcting output coding corrects bias and variance. 12th International Conference on Machine Learning, p 313-321, Tahoe City, CA. San Francisco, 1995.
14. Penny W, Roberts S. Neural network estimations with error bars. Research Report TR-97-1, Department of Electrical and Electronic Engineering, Imperial College, London, 1997.
15. Perrone M. Improving regression estimation: averaging methods for variance reduction with extensions to general convex measure optimization. Ph.D. thesis, Brown University, 1993.
16. Prechelt L. Proben1: A set of neural network benchmark problems and benchmarking rules. Technical Report 21/94. Universitat Karlsruhe, 1994.
17. Seber G, Wild C. Nonlinear regression. John Wiley & Sons; 1989.
18. Sharkey A (editor). Combining artificial neural nets: Ensemble and modular multi-net systems. Springer-Verlag; 1999.
19. Sloane N, Mac Williams F. The theory of error-correcting codes. North-Holland; 1998.
20. Tibshirani R. A comparison of some error estimates for neural network models. Neural Comput 1996;8:152-163.
21. Wei BC. Exponential family nonlinear models. Lecture Notes in Statistics 130. Springer-Verlag; 1998.