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Volumn 2714, Issue , 2003, Pages 201-208

Generalization error analysis for polynomial kernel methods - Algebraic geometrical approach

Author keywords

[No Author keywords available]

Indexed keywords

ALGEBRA; ERRORS; LEARNING SYSTEMS; NEURAL NETWORKS; POLYNOMIALS;

EID: 35248845495     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44989-2_25     Document Type: Article
Times cited : (7)

References (18)
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    • (1964) Automation and Remote Control , vol.25 , pp. 821-837
    • Aizerman, M.A.1    Braverman, E.M.2    Rozonoer, L.I.3
  • 2
    • 0027257001 scopus 로고
    • A universal theorem on learning curves
    • Amari, S.: A universal theorem on learning curves. Neural Networks, 6 (1993) 161-166
    • (1993) Neural Networks , vol.6 , pp. 161-166
    • Amari, S.1
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    • 0000729504 scopus 로고
    • Statistical Theory of Learning Curves under Entropic Loss Criterion
    • Amari, S., Murata, N.: Statistical Theory of Learning Curves under Entropic Loss Criterion. Neural Computation, 5 (1993) 140-153
    • (1993) Neural Computation , vol.5 , pp. 140-153
    • Amari, S.1    Murata, N.2
  • 5
    • 0001160588 scopus 로고
    • What Size Net Gives Valid Generalization?
    • Baum, E.B., Haussler, D.: What Size Net Gives Valid Generalization? Neural Computation, 1 (1989) 151-160
    • (1989) Neural Computation , vol.1 , pp. 151-160
    • Baum, E.B.1    Haussler, D.2
  • 9
    • 0842283738 scopus 로고    scopus 로고
    • Geometry and Learning Curves of Kernel Methods with Polynomial Kernels
    • in press (in Japanese)
    • Ikeda, K.: Geometry and Learning Curves of Kernel Methods with Polynomial Kernels. Trans. of IEICE, J86-D-II (2003) in press (in Japanese).
    • (2003) Trans. of IEICE , vol.J86-D-II
    • Ikeda, K.1
  • 10
    • 33746218431 scopus 로고    scopus 로고
    • Geometry of Admissible Parameter Region in Neural Learning
    • Ikeda, K., Amari, S.: Geometry of Admissible Parameter Region in Neural Learning. IEICE Trans. Fundamentals, E79-A (1996) 409-414
    • (1996) IEICE Trans. Fundamentals , vol.E79-A , pp. 409-414
    • Ikeda, K.1    Amari, S.2
  • 11
    • 0028544395 scopus 로고
    • Network Information Criterions - Determining the Number of Parameters for an Artifcial Neural Network Model
    • Murata, N., Yoshizawa, S., Amari, S.: Network Information Criterions - Determining the Number of Parameters for an Artifcial Neural Network Model. IEEE Trans. Neural Networks, 5 (1994) 865-872
    • (1994) IEEE Trans. Neural Networks , vol.5 , pp. 865-872
    • Murata, N.1    Yoshizawa, S.2    Amari, S.3
  • 12
    • 0041081792 scopus 로고
    • Calculation of the Learning Curve of Bayes Optimal Classification on Algorithm for Learning a Perceptron with Noise
    • Opper, M., Haussier, D.: Calculation of the Learning Curve of Bayes Optimal Classification on Algorithm for Learning a Perceptron with Noise. Proc. 4th Ann. Workshop Comp. Learning Theory (1991) 75-87
    • (1991) Proc. 4th Ann. Workshop Comp. Learning Theory , pp. 75-87
    • Opper, M.1    Haussier, D.2
  • 14
    • 0003652453 scopus 로고    scopus 로고
    • Smola, A.J. et al. (eds.): MIT Press, Cambridge, MA
    • Smola, A.J. et al. (eds.): Advances in Large Margin Classifiers. MIT Press, Cambridge, MA (2000)
    • (2000) Advances in Large Margin Classifiers
  • 16
    • 0021518106 scopus 로고
    • A Theory of the Learnable
    • Valiant, L.G.: A Theory of the Learnable. Communications of ACM, 27 (1984) 1134-1142
    • (1984) Communications of ACM , vol.27 , pp. 1134-1142
    • Valiant, L.G.1
  • 18
    • 0001024505 scopus 로고
    • On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities
    • Vapnik, V.N., Chervonenkis, A.Y.: On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities. Theory of Probability and its Applications, 16 (1971) 264-280
    • (1971) Theory of Probability and Its Applications , vol.16 , pp. 264-280
    • Vapnik, V.N.1    Chervonenkis, A.Y.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.