메뉴 건너뛰기




Volumn 27, Issue 11, 1989, Pages 47-50

Pattern Classification Using Neural Networks

(1)  Lippmann, Richard P a  

a NONE

Author keywords

[No Author keywords available]

Indexed keywords

PATTERN RECOGNITION;

EID: 0024771475     PISSN: 01636804     EISSN: None     Source Type: Journal    
DOI: 10.1109/35.41401     Document Type: Article
Times cited : (612)

References (92)
  • 3
    • 0023843391 scopus 로고
    • Analysis of Hidden Units in a Layered Network Trained to Classify Sonar Targets
    • R. P. Gorman and T. J. Sejnowski, “Analysis of Hidden Units in a Layered Network Trained to Classify Sonar Targets,” Neural Networks, vol. 1, pp. 75-89, 1988.
    • (1988) Neural Networks , vol.1 , pp. 75-89
    • Gorman, R.P.1    Sejnowski, T.J.2
  • 4
    • 0024167373 scopus 로고
    • Experiments for Isolated-Word Recognition with Single and Multi-Layer Perceptrons
    • supp. 1, Abstracts of 1st Annual INNS Meeting, Boston
    • B. Kammerer and W. Kupper, “Experiments for Isolated-Word Recognition with Single and Multi-Layer Perceptrons,” Neural Networks, vol. 1, supp. 1, p. 302, Abstracts of 1st Annual INNS Meeting, Boston, 1988.
    • (1988) Neural Networks , vol.1 , pp. 302
    • Kammerer, B.1    Kupper, W.2
  • 5
    • 0003966401 scopus 로고
    • The Development of the Time-Delay Neural Network Architecture for Speech Recognition
    • Tech. Rep. CMU-CS-88-152, Carnegie-Mellon University
    • K.J. Lang and G. E. Hinton, “The Development of the Time-Delay Neural Network Architecture for Speech Recognition,” Tech. Rep. CMU-CS-88-152, Carnegie-Mellon University, 1988.
    • (1988)
    • Lang, K.J.1    Hinton, G.E.2
  • 6
    • 84936526690 scopus 로고
    • Review of Neural Networks for Speech Recognition
    • R. P. Lippmann, “Review of Neural Networks for Speech Recognition,” Neural Comp., vol. 1 (1), pp. 1-38, 1989.
    • (1989) Neural Comp. , vol.1 , Issue.1 , pp. 1-38
    • Lippmann, R.P.1
  • 7
    • 79952783520 scopus 로고
    • Experiments in Isolated Digit Recognition Using the Multi-Layer Perceptron
    • Tech. Rep. 4073, Malvern, Worcester, Great Britain, Dec.
    • S. M. Peeling and R. K. Moore, “Experiments in Isolated Digit Recognition Using the Multi-Layer Perceptron,” Tech. Rep. 4073, Royal Speech and Radar Estab., Malvern, Worcester, Great Britain, Dec. 1987.
    • (1987) Royal Speech and Radar Estab.
    • Peeling, S.M.1    Moore, R.K.2
  • 8
    • 84909725120 scopus 로고
    • Modularity and Scaling in Large Phonemic Neural Nets
    • Tech. Rep. TR-1-0034, ATR Interpreting Telephony Research Laboratories, Japan, Aug.
    • A. Waibel, H. Sawai, and K. Shikano, “Modularity and Scaling in Large Phonemic Neural Nets,” Tech. Rep. TR-1-0034, ATR Interpreting Telephony Research Laboratories, Japan, Aug. 1988.
    • (1988)
    • Waibel, A.1    Sawai, H.2    Shikano, K.3
  • 9
    • 0023331258 scopus 로고
    • An Introduction to Computing with Neural Nets
    • Apr.
    • R. P. Lippmann, “An Introduction to Computing with Neural Nets,” IEEE ASSP Mag., vol. 4 (2), pp. 4-22, Apr. 1987.
    • (1987) IEEE ASSP Mag. , vol.4 , Issue.2 , pp. 4-22
    • Lippmann, R.P.1
  • 11
    • 84972517864 scopus 로고
    • Discriminant Analysis and Clustering
    • R. Gnanadesikan and J. R. Kettenring, “Discriminant Analysis and Clustering,” Stat. Sci., vol. 4(1), pp. 34-69, 1989.
    • (1989) Stat. Sci. , vol.4 , Issue.1 , pp. 34-69
    • Gnanadesikan, R.1    Kettenring, J.R.2
  • 12
    • 0344956587 scopus 로고
    • Advances in Statistical Pattern Recognition
    • P.A. Devijver and J. Kittler, eds., Series F: Computer and Systems Sciences, NY: Springer Verlag
    • A. K. Jain, “Advances in Statistical Pattern Recognition,” Pattern Recognition Theory and Applications, P.A. Devijver and J. Kittler, eds., pp. 1-19, Series F: Computer and Systems Sciences, vol. 30, NY: Springer Verlag, 1989.
    • (1989) Pattern Recognition Theory and Applications , vol.30 , pp. 1-19
    • Jain, A.K.1
  • 13
    • 0004141541 scopus 로고
    • Connectionist Learning Procedures
    • Tech. Rep. CMU-CS-87-115, Carnegie Mellon University, Computer Science Department, June
    • G. E. Hinton, “Connectionist Learning Procedures,” Tech. Rep. CMU-CS-87-115, Carnegie Mellon University, Computer Science Department, June 1987.
    • (1987)
    • Hinton, G.E.1
  • 14
  • 15
    • 84941534837 scopus 로고
    • Adaptive Knowledge Processing
    • Ch. II, AFCEA International Press, Fairfax, VA
    • J. Pearson and R. Lippmann, ed., “Adaptive Knowledge Processing,” DARPA Neural Network Study, Ch. II, pp. 55-178, AFCEA International Press, Fairfax, VA, 1988.
    • (1988) DARPA Neural Network Study , pp. 55-178
    • Pearson, J.1    Lippmann, R.2
  • 16
    • 0002127281 scopus 로고
    • What Size Net Gives Valid Generalization?
    • D.S. Touretzky, ed., San Mateo, CA: Morgan Kauffman
    • E. B. Baum and D. Haussler, “What Size Net Gives Valid Generalization?” Advances in Neural Info. Processing Syst. 1, D.S. Touretzky, ed., San Mateo, CA: Morgan Kauffman, 1989.
    • (1989) Advances in Neural Info. Processing Syst , vol.1
    • Baum, E.B.1    Haussler, D.2
  • 19
    • 0020719921 scopus 로고
    • Candide's Practical Principles of Experimental Pattern Recognition
    • G. Nagy, “Candide's Practical Principles of Experimental Pattern Recognition,” IEEE Trans, on Pattern Anal, and Machine Intel., vol. PAMI-5(2), pp. 199-200, 1983
    • (1983) IEEE Trans, on Pattern Anal, and Machine Intel. , vol.PAMI-5 , Issue.2 , pp. 199-200
    • Nagy, G.1
  • 20
    • 0000646059 scopus 로고
    • Learning Internal Representations by Error Propagation
    • D.E. Rumelhart and J.L. McClelland, eds., Ch. 8, Cambridge, MA: MIT Press
    • D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning Internal Representations by Error Propagation,” Parallel Distributed Processing, D.E. Rumelhart and J.L. McClelland, eds., Ch. 8, Cambridge, MA: MIT Press, 1986.
    • (1986) Parallel Distributed Processing
    • Rumelhart, D.E.1    Hinton, G.E.2    Williams, R.J.3
  • 21
    • 0001578518 scopus 로고
    • A Learning Algorithm for Boltzmann Machines
    • D. H. Ackley, G. E. Hinton, and T.J. Sejnowski, “A Learning Algorithm for Boltzmann Machines,” Cognitive Science, vol. 9, pp. 147-160, 1985.
    • (1985) Cognitive Science , vol.9 , pp. 147-160
    • Ackley, D.H.1    Hinton, G.E.2    Sejnowski, T.J.3
  • 23
    • 0023417432 scopus 로고
    • Simplifying Decision Trees
    • J. R. Quinlan, “Simplifying Decision Trees,” Int'l. J. Man-Machine Studies, vol. 27, pp. 221-234, 1987.
    • (1987) Int'l. J. Man-Machine Studies , vol.27 , pp. 221-234
    • Quinlan, J.R.1
  • 24
    • 0023513717 scopus 로고
    • Learning, Invariance, and Generalization in High-Order Networks
    • Dec.
    • C. L. Giles and T. Maxwell, “Learning, Invariance, and Generalization in High-Order Networks,” Applied Optics, vol. 26, pp. 4,972-4,978, Dec. 1987.
    • (1987) Applied Optics , vol.26 , pp. 4,972-4,978
    • Giles, C.L.1    Maxwell, T.2
  • 25
    • 0003728220 scopus 로고
    • Learning Machines
    • McGraw Hill, N.Y.
    • N. J. Nilsson, “Learning Machines,” McGraw Hill, N.Y., 1965.
    • (1965)
    • Nilsson, N.J.1
  • 26
    • 0004096959 scopus 로고
    • Self-Organizing Methods in Modeling
    • Marcel Dekker
    • S. Farlow, “Self-Organizing Methods in Modeling,” Marcel Dekker, 1984.
    • (1984)
    • Farlow, S.1
  • 27
    • 0003592281 scopus 로고
    • Kernel Discriminant Analysis
    • John Wiley and Sons Ltd., New York, NY
    • D. J. Hand, ed., “Kernel Discriminant Analysis,” John Wiley and Sons Ltd., New York, NY, 1982.
    • (1982)
    • Hand, D.J.1
  • 29
    • 84913720968 scopus 로고
    • A Multiple-Map Model for Pattern Classification
    • A. Rojer and E. Schwartz, “A Multiple-Map Model for Pattern Classification,” Neural Computation, vol. 1(1), pp. 104-115, 1989.
    • (1989) Neural Computation , vol.1 , Issue.1 , pp. 104-115
    • Rojer, A.1    Schwartz, E.2
  • 31
    • 0001886167 scopus 로고
    • Fast Learning in Multi-Resolution Hierarchies
    • Tech. Rep. YALEU/DCS/RR-681, Yale Computer Science Department, New Haven, CT, Feb.
    • J. Moody, “Fast Learning in Multi-Resolution Hierarchies,” Tech. Rep. YALEU/DCS/RR-681, Yale Computer Science Department, New Haven, CT, Feb. 1989.
    • (1989)
    • Moody, J.1
  • 32
    • 77956167860 scopus 로고
    • Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks
    • Royal Speech and Radar Establishment, Malvern, Worcester, Great Britain, Mar.
    • D. S. Broomhead and D. Lowe, “Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks,” Tech. Rep. RSRE, Memo. no. 4, 148, Royal Speech and Radar Establishment, Malvern, Worcester, Great Britain, Mar. 1988.
    • (1988) Tech. Rep. RSRE, Memo. , Issue.4 , pp. 148
    • Broomhead, D.S.1    Lowe, D.2
  • 33
    • 6344280206 scopus 로고
    • Neural Networks and Radial Basis Functions in Classifying Static Speech Patterns
    • Tech. Rep. CUED/FINFENG/TR 22, Cambridge University Engineering Department
    • M. Niranjan and F. Fallside, “Neural Networks and Radial Basis Functions in Classifying Static Speech Patterns,” Tech. Rep. CUED/FINFENG/TR 22, Cambridge University Engineering Department, 1988.
    • (1988)
    • Niranjan, M.1    Fallside, F.2
  • 34
    • 0024922715 scopus 로고
    • Phoneme Classification Experiments using Radial Basis Functions
    • IEEE, Washington, DC, June
    • S. Renals and R. Rohwer, “Phoneme Classification Experiments using Radial Basis Functions,” Proc. Int'l. Joint Conf. on Neural Networks, pp. 1.461-1.467, IEEE, Washington, DC, June 1989.
    • (1989) Proc. Int'l. Joint Conf. on Neural Networks , pp. 1.461-1.467
    • Renals, S.1    Rohwer, R.2
  • 35
    • 0043023536 scopus 로고
    • Efficient Algorithms with Neural Network Behavior
    • S. M. Omohundro, “Efficient Algorithms with Neural Network Behavior,” Complex Systems, vol. 1, pp. 273-347, 1987.
    • (1987) Complex Systems , vol.1 , pp. 273-347
    • Omohundro, S.M.1
  • 36
    • 0343296749 scopus 로고
    • Neural Net and Traditional Classifiers
    • D. Anderson, ed., NY: American Institute of Physics
    • W. M. Huang and R. P. Lippmann, “Neural Net and Traditional Classifiers,” Neural Info. Processing Syst., D. Anderson, ed., pp. 387-396, NY: American Institute of Physics, 1988.
    • (1988) Neural Info. Processing Syst. , pp. 387-396
    • Huang, W.M.1    Lippmann, R.P.2
  • 37
    • 0023855839 scopus 로고
    • An Introduction to Neural Computing
    • T. Kohonen, “An Introduction to Neural Computing,” Neural Networks, vol. 1, pp. 3-16, 1988.
    • (1988) Neural Networks , vol.1 , pp. 3-16
    • Kohonen, T.1
  • 38
    • 0020415469 scopus 로고
    • A Neural Model for Category Learning
    • D L. Reilly, L.N. Cooper, and C. Elbaum, “A Neural Model for Category Learning,” Bio. Cybernetics, vol. 45, pp. 35-41, 1982.
    • (1982) Bio. Cybernetics , vol.45 , pp. 35-41
    • Reilly, D.L.1    Cooper, L.N.2    Elbaum, C.3
  • 40
    • 84973857317 scopus 로고
    • ART 2: Self-Organization of Stable Category Recognition Codes for Analog Input Patterns
    • G. A. Carpenter and S. Grossberg, “ART 2: Self-Organization of Stable Category Recognition Codes for Analog Input Patterns,” Applied Optics, vol. 26, pp. 4,919-4,930, 1987.
    • (1987) Applied Optics , vol.26 , pp. 4,919-4,930
    • Carpenter, G.A.1    Grossberg, S.2
  • 41
    • 0022909661 scopus 로고
    • Toward Memory-Based Reasoning
    • Dec.
    • C Stanfill and D. Waltz, “Toward Memory-Based Reasoning,” Commun. of the ACM, vol. 29(12), pp. 1,213-1,228, Dec. 1986.
    • (1986) Commun. of the ACM , vol.29 , Issue.12 , pp. 1,213-1,228
    • Stanfill, C.1    Waltz, D.2
  • 42
    • 70449944192 scopus 로고
    • Very Large Database Applications of the Connection Machine System
    • D. Waltz, C. Stanfill, S. Smith, and R. Thau, “Very Large Database Applications of the Connection Machine System,” Proc. Nat'l. Computer Conf., pp. 159-165, 1987.
    • (1987) Proc. Nat'l. Computer Conf. , pp. 159-165
    • Waltz, D.1    Stanfill, C.2    Smith, S.3    Thau, R.4
  • 43
    • 0024173775 scopus 로고
    • Alternative Generalizers to Neural Nets
    • supp. 1, Abstracts of 1st Annual INNS Meeting, Boston
    • D. Wolpert, “Alternative Generalizers to Neural Nets,” Neural Networks, vol. 1, supp. 1, p. 474, Abstracts of 1st Annual INNS Meeting, Boston, 1988.
    • (1988) Neural Networks , vol.1 , pp. 474
    • Wolpert, D.1
  • 44
    • 0024490816 scopus 로고
    • The Recent Excitement about Neural Nets
    • R. Crick, “The Recent Excitement about Neural Nets,” Nature, vol. 337, pp. 129-132, 1989.
    • (1989) Nature , vol.337 , pp. 129-132
    • Crick, R.1
  • 45
    • 0000383868 scopus 로고
    • Parallel Networks that Learn to Pronounce English Text
    • T. J. Sejnowski and C. M. Rosenberg, “Parallel Networks that Learn to Pronounce English Text,” Complex Systems, vol. 1, pp. 145-168, 1987.
    • (1987) Complex Systems , vol.1 , pp. 145-168
    • Sejnowski, T.J.1    Rosenberg, C.M.2
  • 46
    • 0023803244 scopus 로고
    • Predicting the Secondary Structure of Globular Proteins using Neural Network Models
    • N. Qian and T. J. Sejnowski, “Predicting the Secondary Structure of Globular Proteins using Neural Network Models,” J. Molecular Biology, vol. 202, pp. 865-884, 1988.
    • (1988) J. Molecular Biology , vol.202 , pp. 865-884
    • Qian, N.1    Sejnowski, T.J.2
  • 47
    • 0342899009 scopus 로고
    • A Neural Network that Learns to Play Backgammon
    • D. Anderson, ed., American Institute of Physics, New York
    • G. Tesauro and T. J. Sejnowski, “A Neural Network that Learns to Play Backgammon,” Neural Information Processing Systems, D. Anderson, ed., pp. 794-803, American Institute of Physics, New York, 1988.
    • (1988) Neural Information Processing Systems , pp. 794-803
    • Tesauro, G.1    Sejnowski, T.J.2
  • 48
    • 0023877474 scopus 로고
    • A Back Propagation Programmed Network that Simulates Response Properties of a Subset of Posterior Parietal Neurons
    • D. Zipser and R. A. Andersen, “A Back Propagation Programmed Network that Simulates Response Properties of a Subset of Posterior Parietal Neurons,” Nature, vol. 331, pp. 679-684, 1988.
    • (1988) Nature , vol.331 , pp. 679-684
    • Zipser, D.1    Andersen, R.A.2
  • 49
    • 0004660329 scopus 로고
    • Adaptive Neural-Net Processing for Signal Detection in Non-Gaussian Noise
    • D.S. Touretzky, ed., San Mateo, CA: Morgan Kauffman
    • R. P. Lippmann and P. E. Beckman, “Adaptive Neural-Net Processing for Signal Detection in Non-Gaussian Noise,” Advances in Neural Info. Processing Syst. 1, D.S. Touretzky, ed., San Mateo, CA: Morgan Kauffman, 1989.
    • (1989) Advances in Neural Info. Processing Syst. , vol.1
    • Lippmann, R.P.1    Beckman, P.E.2
  • 51
    • 84913443851 scopus 로고
    • Knowledge Representation in Connectionist Networks
    • Tech. Rep., Morristown, New Jersey, Feb.
    • S. J. Hanson and D. J. Burr, “Knowledge Representation in Connectionist Networks,” Tech. Rep., Bell Communications Research, Morristown, New Jersey, Feb. 1987.
    • (1987) Bell Communications Research
    • Hanson, S.J.1    Burr, D.J.2
  • 52
    • 84941544116 scopus 로고
    • A Pattern Recognition Approach to Understanding the Multi-Layer Perceptron
    • Memo. 3,936, July
    • I. D. Longstaff and J. F. Cross, “A Pattern Recognition Approach to Understanding the Multi-Layer Perceptron,” Memo. 3,936, Royal Signals and Radar Establishment, July 1986.
    • (1986) Royal Signals and Radar Establishment
    • Longstaff, I.D.1    Cross, J.F.2
  • 53
    • 0002748218 scopus 로고
    • How Neural Nets Work
    • D. Anderson, ed., American Institute of Physics, New York
    • A Lapedes and R. Farber, “How Neural Nets Work,” Neural Information Processing Systems, D. Anderson, ed., pp. 442-456, American Institute of Physics, New York, 1988.
    • (1988) Neural Information Processing Systems , pp. 442-456
    • Lapedes, A.1    Farber, R.2
  • 55
    • 0024861871 scopus 로고
    • Approximation by Superpositions of a Sigmoidal Function
    • “To Appear
    • G. Cybenko, “Approximation by Superpositions of a Sigmoidal Function,” Mathematics of Control, Signals, and Systems, 2(4), 1989, To Appear.
    • (1989) Mathematics of Control, Signals, and Systems , vol.2 , Issue.4
    • Cybenko, G.1
  • 57
    • 84941544118 scopus 로고
    • Back-Propagation Separates when Perceptrons Do
    • Tech. Rep. SYCON-88-12, Rutgers Center for Systems and Control, December
    • E. D Sontag and H. J Sussmann, “Back-Propagation Separates when Perceptrons Do,” Tech. Rep. SYCON-88-12, Rutgers Center for Systems and Control, December 1988.
    • (1988)
    • Sontag, E.D.1    Sussmann, H.J.2
  • 58
    • 0005233671 scopus 로고
    • Strategies for Teaching Layered Networks Classification Tasks
    • D. Anderson, editor, American Institute of Physics
    • B. S. Winner and J. S. Denker, “Strategies for Teaching Layered Networks Classification Tasks,” In D. Anderson, editor, Neural Information Processing Systems, pp. 850-859, American Institute of Physics, 1988.
    • (1988) Neural Information Processing Systems , pp. 850-859
    • Winner, B.S.1    Denker, J.S.2
  • 60
    • 0024881235 scopus 로고
    • Parallelism, Hierarchy, Scaling in Time-Delay Neural Networks for Spotting Japanese Phonemes/CVsyllables
    • IEEE, Washington DC, June
    • H. Sawai et al., “Parallelism, Hierarchy, Scaling in Time-Delay Neural Networks for Spotting Japanese Phonemes/CVsyllables,” In Proceedings International Joint Conference on Neural Networks, pp. 11.81-11.88, IEEE, Washington DC, June 1989.
    • (1989) Proceedings International Joint Conference on Neural Networks , pp. 11.81-11.88
    • Sawai, H.1
  • 61
    • 0002906163 scopus 로고
    • Improving the Convergence of Back-Propagation Learning with Second Order Methods
    • D. Touretzky, G. Hinton, and T. Sejnowski, eds., San Mateo, CA: Morgan Kauffman
    • S. Becker and Y. Le Cun, “Improving the Convergence of Back-Propagation Learning with Second Order Methods,” Proc. of the 1988 Connectionist Models Summer School, D. Touretzky, G. Hinton, and T. Sejnowski, eds., pp. 29-37, San Mateo, CA: Morgan Kauffman, 1989.
    • (1989) Proc. of the 1988 Connectionist Models Summer School , pp. 29-37
    • Becker, S.1    Le Cun, Y.2
  • 62
    • 0001031887 scopus 로고
    • An Adaptive Training Algorithm for Back-Propagation Networks
    • L. W. Chan and F. Fallside, “An Adaptive Training Algorithm for Back-Propagation Networks,” Comp. Speech and Language, vol. 2, pp. 205-218, 1987.
    • (1987) Comp. Speech and Language , vol.2 , pp. 205-218
    • Chan, L.W.1    Fallside, F.2
  • 63
    • 0003000735 scopus 로고
    • Faster-Learning Variations on Back-Propagation: An Empirical Study
    • D. Touretzky, G. Hinton, and T. Sejnowski, eds., San Mateo, CA: Morgan Kauffman
    • S. E. Fahlman, “Faster-Learning Variations on Back-Propagation: An Empirical Study,” Proc. of the 1988 Connectionist Models Summer School, D. Touretzky, G. Hinton, and T. Sejnowski, eds., pp. 38-51, San Mateo, CA: Morgan Kauffman, 1989.
    • (1989) Proc. of the 1988 Connectionist Models Summer School , pp. 38-51
    • Fahlman, S.E.1
  • 64
    • 0040864042 scopus 로고
    • Minkowski-r Back-Propagation: Learning in Connectionist Models with Non-Euclidean Error Signals
    • D. Anderson, ed., NY: American Institute of Physics
    • S. J. Hanson and D. J. Burr, “Minkowski-r Back-Propagation: Learning in Connectionist Models with Non-Euclidean Error Signals,” Neural Information Processing Systems, D. Anderson, ed., pp. 348-357, NY: American Institute of Physics, 1988.
    • (1988) Neural Information Processing Systems , pp. 348-357
    • Hanson, S.J.1    Burr, D.J.2
  • 65
    • 0024137490 scopus 로고
    • Increased Rates of Convergence Through Learning Rate Adaptation
    • R. A.Jacobs, “Increased Rates of Convergence Through Learning Rate Adaptation,” Neural Networks, vol. 1, pp. 295-307, 1988.
    • (1988) Neural Networks , vol.1 , pp. 295-307
    • Jacobs, R.A.1
  • 67
    • 3743062328 scopus 로고
    • Learning Algorithms for Connectionist Networks: Applied Gradient Methods of Nonlinear Optimization
    • Tech. Rep. MS-CIS-87-51, Line Lab 72, University of Pennsylvania, June
    • R. L. Watrous, “Learning Algorithms for Connectionist Networks: Applied Gradient Methods of Nonlinear Optimization,” Tech. Rep. MS-CIS-87-51, Line Lab 72, University of Pennsylvania, June 1986.
    • (1986)
    • Watrous, R.L.1
  • 68
    • 0000991092 scopus 로고
    • Some Comparisons of Constraints for Minimal Network Construction with Back-Propagation
    • D. S. Touretzky, ed., San Mateo, CA: Morgan Kauffman
    • S. J. Hanson and L. Y. Pratt, “Some Comparisons of Constraints for Minimal Network Construction with Back-Propagation,” Advances in Neural Information Processing Systems 1, D. S. Touretzky, ed., San Mateo, CA: Morgan Kauffman, 1989.
    • (1989) Advances in Neural Information Processing Systems , vol.1
    • Hanson, S.J.1    Pratt, L.Y.2
  • 69
    • 0000900876 scopus 로고
    • Skeletonization: a Technique for Trimming the Fat from a Network Via Relevance Assessment
    • D. S. Touretzky, ed., Morgan Kauffman, San Mateo, CA
    • M. C. Mozer and P. Smolensky, “Skeletonization: a Technique for Trimming the Fat from a Network Via Relevance Assessment,” Advances in Neural Information Processing Systems 7, D. S. Touretzky, ed., Morgan Kauffman, San Mateo, CA, 1989.
    • (1989) Advances in Neural Information Processing Systems , vol.7
    • Mozer, M.C.1    Smolensky, P.2
  • 70
    • 33744584654 scopus 로고
    • Induction of Decision Trees
    • J R. Quinlan, “Induction of Decision Trees,” Machine Learning, vol. 1, pp. 81-106, 1986.
    • (1986) Machine Learning , vol.1 , pp. 81-106
    • Quinlan, J.R.1
  • 71
    • 84941529537 scopus 로고
    • An Empirical Comparison of ID3 and Back-Propagation
    • Tech. Rep. TR CS-88-14, Vanderbilt University, Nashville, TN
    • D. H. Fisher and K. B. McKusick, “An Empirical Comparison of ID3 and Back-Propagation,” Tech. Rep. TR CS-88-14, Department of Computer Science, Vanderbilt University, Nashville, TN, 1988.
    • (1988) Department of Computer Science
    • Fisher, D.H.1    McKusick, K.B.2
  • 72
    • 63249112814 scopus 로고
    • Dimensionality and Sample Size Consideration in Pattern Recognition Practice
    • P. R. Krishnaiah and L. N. Kanal, eds., North Holland
    • A. K. Jain and B. Chandrasekaran, “Dimensionality and Sample Size Consideration in Pattern Recognition Practice,” Handbook of Statistics: Vol. 2, P. R. Krishnaiah and L. N. Kanal, eds., pp. 835-855, North Holland, 1982.
    • (1982) Handbook of Statistics , vol.2 , pp. 835-855
    • Jain, A.K.1    Chandrasekaran, B.2
  • 73
    • 0018491795 scopus 로고
    • A Problem of Dimensionality: a Simple Example
    • G. V. Trunk, “A Problem of Dimensionality: a Simple Example,” IEEE Trans, on Pattern Analysis and Machine Intel., vol. PAMI-1, no. 3, pp. 306-307, 1979.
    • (1979) IEEE Trans, on Pattern Analysis and Machine Intel. , vol.PAMI-1 , Issue.3 , pp. 306-307
    • Trunk, G.V.1
  • 74
    • 0000629975 scopus 로고
    • Cross-Validation Choice and Assessment of Statistical Predictions
    • M. Stone, “Cross-Validation Choice and Assessment of Statistical Predictions,” J. of the Royal Statistical Soc., vol. B-36, pp. 111-147, 1974.
    • (1974) J. of the Royal Statistical Soc. , vol.B-36 , pp. 111-147
    • Stone, M.1
  • 76
    • 0024627518 scopus 로고
    • Inferring Decision Trees using the Minimum Descriptive Length Principle
    • to appear
    • J. R. Quinlan and R. Rivest, “Inferring Decision Trees using the Minimum Descriptive Length Principle,” Info, and Computation, to appear, 1989.
    • (1989) Info, and Computation
    • Quinlan, J.R.1    Rivest, R.2
  • 77
    • 0001098776 scopus 로고
    • A Universal Prior for Integers and Estimation by Minimum Descriptive Length
    • J. Rissanen, “A Universal Prior for Integers and Estimation by Minimum Descriptive Length,” The Annals of Statistics, vol. 11, pp. 416431, 1983.
    • (1983) The Annals of Statistics , vol.11 , pp. 416-431
    • Rissanen, J.1
  • 78
    • 0002167090 scopus 로고
    • Predicted Squared Error: a Criterion for Automatic Model Selection
    • S. J. Farlow, ed., NY: Marcel Dekker
    • A. R. Barron, “Predicted Squared Error: a Criterion for Automatic Model Selection,” Self-Organizing Methods in Modeling, S. J. Farlow, ed., pp. 87-103, NY: Marcel Dekker, 1984.
    • (1984) Self-Organizing Methods in Modeling , pp. 87-103
    • Barron, A.R.1
  • 80
    • 0016765357 scopus 로고
    • On Optimal Nonlinear Associative Recall
    • T. Poggio, “On Optimal Nonlinear Associative Recall,” Bio. Cybernetics, vol. 19, p. 201, 1975.
    • (1975) Bio. Cybernetics , vol.19 , pp. 201
    • Poggio, T.1
  • 81
    • 0003324736 scopus 로고
    • Adaptive Learning Networks: Development and Application in the United States of Algorithms Related to GMDH
    • S. J. Farlow, ed., NY: Marcel Dekker
    • A. R. Barron, “Adaptive Learning Networks: Development and Application in the United States of Algorithms Related to GMDH,” Self-Organizing Methods in Modeling, S. J. Farlow, ed., pp. 25-65, NY: Marcel Dekker, 1984.
    • (1984) Self-Organizing Methods in Modeling , pp. 25-65
    • Barron, A.R.1
  • 82
    • 0040537338 scopus 로고
    • Exploiting Chaos to Predict the Future and Reduce Noise
    • Tech. Rep. LA-UR-88-901, Los Alamos National Laboratory, Los Alamos, New Mexico, Mar.
    • J. D. Farmer and J.J. Sidorowich, “Exploiting Chaos to Predict the Future and Reduce Noise,” Tech. Rep. LA-UR-88-901, Los Alamos National Laboratory, Los Alamos, New Mexico, Mar. 1988.
    • (1988)
    • Farmer, J.D.1    Sidorowich, J.J.2
  • 85
    • 0018043463 scopus 로고
    • Classification and Data Analysis in Vector Space
    • B. G. Batchelor, ed., ch. 4, London: Plenum Press
    • B. G. Batchelor, “Classification and Data Analysis in Vector Space,” Pattern Recognition, B. G. Batchelor, ed., ch. 4, pp. 67-116, London: Plenum Press, 1978.
    • (1978) Pattern Recognition , pp. 67-116
    • Batchelor, B.G.1
  • 86
    • 0023257780 scopus 로고
    • Disjunctive Models of Boolean Category Learning
    • S. E. Hampson and D. J. Volper, “Disjunctive Models of Boolean Category Learning,” Bio. Cybernetics, vol. 56, pp. 121-137, 1987.
    • (1987) Bio. Cybernetics , vol.56 , pp. 121-137
    • Hampson, S.E.1    Volper, D.J.2
  • 87
    • 33745290584 scopus 로고
    • Pattern Class Degeneracy in an Unrestricted Storage Density Memory
    • American Institute of Physics
    • C. L. Scofield, D. L. Reilly, C. Elbaum, and L. N. Cooper, “Pattern Class Degeneracy in an Unrestricted Storage Density Memory,” Neural Info. Processing Syst., pp. 674-682, American Institute of Physics, 1988.
    • (1988) Neural Info. Processing Syst. , pp. 674-682
    • Scofield, C.L.1    Reilly, D.L.2    Elbaum, C.3    Cooper, L.N.4
  • 88
    • 84912870339 scopus 로고
    • Appendix G: Risk Analysis
    • Fairfax, VA: AFCEA International Press
    • E. Collins, S. Ghosh, and C. Scofield, “Appendix G: Risk Analysis,” DARPA Neural Network Study, pp. 429-443, Fairfax, VA: AFCEA International Press, 1988.
    • (1988) DARPA Neural Network Study , pp. 429-443
    • Collins, E.1    Ghosh, S.2    Scofield, C.3
  • 89
    • 0024944137 scopus 로고
    • Comparing Different Neural Network Architectures for Classifying Handwritten Digits
    • Washington DC: IEEE, June
    • I. Guyon, I. Poujand, L. Personnaz, and G. Dreyfus, “Comparing Different Neural Network Architectures for Classifying Handwritten Digits,” Proc., Int'l. Joint Conf. on Neural Networks, pp. 11.127-11.132, Washington DC: IEEE, June 1989.
    • (1989) Proc., Int'l. Joint Conf. on Neural Networks , pp. 11.127-11.132
    • Guyon, I.1    Poujand, I.2    Personnaz, L.3    Dreyfus, G.4
  • 90
    • 0042787444 scopus 로고
    • Classifiers: Adaptive Modules in Pattern Recognition Systems
    • Master's thesis, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, Cambridge, MA, May
    • Y. Lee, “Classifiers: Adaptive Modules in Pattern Recognition Systems,” Master's thesis, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, Cambridge, MA, May 1989.
    • (1989)
    • Lee, Y.1
  • 91
    • 0008417713 scopus 로고
    • Practical Characteristics of Neural Network and Conventional Pattern Classifiers on Artificial and Speech Problems
    • Denver, CO, Nov.
    • Y. Lee and R. P. Lippmann, “Practical Characteristics of Neural Network and Conventional Pattern Classifiers on Artificial and Speech Problems,” Proc., Neural Info. Processing Syst.—Natural and Synthetic Conf., Denver, CO, Nov. 1989.
    • (1989) Proc., Neural Info. Processing Syst.—Natural and Synthetic Conf.
    • Lee, Y.1    Lippmann, R.P.2
  • 92
    • 0003363509 scopus 로고
    • Self Organizing Neural Networks for the Identification Problem
    • D. S. Touretzky, ed., San Mateo, CA: Morgan Kauffman
    • M. F. Tenorio and W. T. Lee, “Self Organizing Neural Networks for the Identification Problem,” Advances in Neural Information Processing Systems 1, D. S. Touretzky, ed., pp. 57-64, San Mateo, CA: Morgan Kauffman, 1989.
    • (1989) Advances in Neural Information Processing Systems , vol.1 , pp. 57-64
    • Tenorio, M.F.1    Lee, W.T.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.