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

Volumn 1208, Issue , 1997, Pages 260-271

Confidence estimates of classification accuracy on new examples

(1) Shawe Taylor, John a

a UNIVERSITY OF LONDON (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; COMPUTERS;

BENEFICIAL EFFECTS; CLASSIFICATION ACCURACY; GENERALIZATION PERFORMANCE; INDEPENDENT MODEL; LARGE MARGINS; OUTPUT VALUES; TEST EXAMPLES; TRAINING EXAMPLE;

COMPUTATION THEORY;

EID: 24044490031 PISSN: 03029743 EISSN: 16113349 Source Type: Book Series
DOI: 10.1007/3-540-62685-9_22 Document Type: Conference Paper

Times cited : (4)

References (9)

1
- 0027802035
- Scale-sensitive Dimensions, Uniform Convergence, and Learnability
- in
- Noga Alon, Shai Ben-David, Nicolò Cesa-Bianchi, David Haussler, "Scale-sensitive Dimensions, Uniform Convergence, and Learnability," in Proceedings of the Conference on Foundations of Computer Science (FOCS), 1993.
- (1993) Proceedings of the Conference on Foundations of Computer Science (FOCS)
- Alon, N.¹ Ben-David, S.² Cesa-Bianchi, N.³ Haussler, D.⁴

2
- 84949291833
- Also to appear in .
- Also to appear in Journal of the ACM.
- Journal of the ACM

3
- 0041805140
- A Result of Vapnik with Applications
- Martin Anthony and John Shawe-Taylor, "A Result of Vapnik with Applications," Discrete Applied Mathematics, 47, 207-217, (1993).
- (1993) Discrete Applied Mathematics , vol.47 , pp. 207-217
- Anthony, M.¹ Shawe-Taylor, J.²

4
- 0002389877
- The Sample Complexity of Pattern Classification with Neural Networks: The Size of the Weights is More Important than the Size of the Network
- Department of Systems Engineering. Australian National University, May
- Peter Bartlett, "The Sample Complexity of Pattern Classification with Neural Networks: the Size of the Weights is More Important than the Size of the Network," Technical Report, Department of Systems Engineering. Australian National University, May 1996.
- (1996) Technical Report
- Bartlett, P.¹

5
- 0026966646
- A Training Algorithm for Optimal Margin Classifiers
- Pittsburgh ACM
- Bernhard E. Boser, Isabelle M. Guyon, and Vladimir N. Vapnik, "A Training Algorithm for Optimal Margin Classifiers," pages 144-152 in Proceedings of the Fifth Annual Workshop on Computational Learning Theory, Pittsburgh ACM, (1992)
- (1992) Proceedings of the Fifth Annual Workshop on Computational Learning Theory , pp. 144-152
- Boser, B.E.¹ Guyon, I.M.² Vapnik, V.N.³

6
- 0003748256
- Ph.D. Thesis, Caltech
- D.J.C. MacKay, Bayesian Methods for Adaptive Models, Ph.D. Thesis, Caltech, 1991.
- (1991) Bayesian Methods for Adaptive Models
- MacKay, D.J.C.¹

7
- 0003570278
- NeuroCOLT Technical Report, NC-TR-96-51
- John Shawe-Taylor, Peter Bartlett, Robert Williamson and Martin Anthony, Structural Risk Minimization over Data-Dependent Hierarchies, NeuroCOLT Technical Report, NC-TR-96-51.
- Structural Risk Minimization over Data-Dependent Hierarchies
- Shawe-Taylor, J.¹ Bartlett, P.² Williamson, R.³ Anthony, M.⁴

8
- 0003969585
- Springer-Verlag, New York
- Vladimir N. Vapnik, Estimation of Dependences Based on Empirical Data, Springer-Verlag, New York, 1982.
- (1982) Estimation of Dependences Based on Empirical Data
- Vapnik, V.N.¹

9
- 0003450542
- Springer-Verlag, New York
- Vladimir N. Vapnik, The Nature of Statistical Learning Theory, Springer-Verlag, New York, 1995.
- (1995) The Nature of Statistical Learning Theory
- Vapnik, V.N.¹

* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.