Skip to main content
SpringerLink
Log in
Book cover

Challenges for Computational Intelligence pp 221–259Cite as

The Science of Pattern Recognition. Achievements and Perspectives

  • Chapter

Part of the book series: Studies in Computational Intelligence ((SCI,volume 63))

Summary

Automatic pattern recognition is usually considered as an engineering area which focusses on the development and evaluation of systems that imitate or assist humans in their ability of recognizing patterns. It may, however, also be considered as a science that studies the faculty of human beings (and possibly other biological systems) to discover, distinguish, characterize patterns in their environment and accordingly identify new observations. The engineering approach to pattern recognition is in this view an attempt to build systems that simulate this phenomenon. By doing that, scientific understanding is gained of what is needed in order to recognize patterns, in general.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. A.G. Arkadev and E.M. Braverman. Computers and Pattern Recognition. Thompson, Washington, DC, 1966.

    Google Scholar 

  2. M. Basu and T.K. Ho, editors. Data Complexity in Pattern Recognition. Springer, 2006.

    Google Scholar 

  3. R. Bergmann. Developing Industrial Case-Based Reasoning Applications. Springer, 2004.

    Google Scholar 

  4. C.M. Bishop. Neural Networks for Pattern Recognition. Oxford University Press, 1995.

    Google Scholar 

  5. H. Bunke. Recent developments in graph matching. In International Conference on Pattern Recognition, volume 2, pages 117-124, 2000.

    Google Scholar 

  6. H. Bunke, S. Günter, and X. Jiang. Towards bridging the gap between statistical and structural pattern recognition: Two new concepts in graph matching. In International Conference on Advances in Pattern Recognition, pages 1-11, 2001.

    Google Scholar 

  7. H. Bunke and K. Shearer. A graph distance metric based on the maximal common subgraph. Pattern Recognition Letters, 19(3-4):255-259, 1998.

    Article  MATH  Google Scholar 

  8. V.S. Cherkassky and F. Mulier. Learning from data: Concepts, Theory and Methods. John Wiley & Sons, Inc., New York, NY, USA, 1998.

    MATH  Google Scholar 

  9. T.M. Cover. Geomerical and statistical properties of systems of linear inequalities with applications in pattern recognition. IEEE Transactions on Electronic Computers, EC-14:326-334, 1965.

    Article  Google Scholar 

  10. T.M. Cover and P.E. Hart. Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1):21-27, 1967.

    Article  MATH  Google Scholar 

  11. T.M. Cover and J.M. van Campenhout. On the possible orderings in the measurement selection problem. IEEE Transactions on Systems, Man, and Cybernetics, SMC-7(9):657-661, 1977.

    Article  Google Scholar 

  12. I.M. de Diego, J.M. Moguerza, and A. Muñoz. Combining kernel information for support vector classification. In Multiple Classifier Systems, pages 102-111. Springer-Verlag, 2004.

    Google Scholar 

  13. A. Dempster, N. Laird, and D. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, 39(1):1-38, 1977.

    MATH  MathSciNet  Google Scholar 

  14. L. Devroye, L. Györfi, and G. Lugosi. A Probabilistic Theory of Pattern Recognition. Springer-Verlag, 1996.

    Google Scholar 

  15. R.O. Duda, P.E. Hart, and D.G. Stork. Pattern Classification. John Wiley & Sons, Inc., 2nd edition, 2001.

    Google Scholar 

  16. R.P.W. Duin. Four scientific approaches to pattern recognition. In Fourth Quinquennial Review 1996-2001. Dutch Society for Pattern Recognition and Image Processing, pages 331-337. NVPHBV, Delft, 2001.

    Google Scholar 

  17. R.P.W. Duin and E. Pekalska. Open issues in pattern recognition. In Computer Recognition Systems, pages 27-42. Springer, Berlin, 2005.

    Chapter  Google Scholar 

  18. R.P.W. Duin, E. Pękalska, P. Paclík, and D.M.J. Tax. The dissimilarity representation, a basis for domain based pattern recognition? In L. Gold-farb, editor, Pattern representation and the future of pattern recognition, ICPR 2004 Workshop Proceedings, pages 43-56, Cambridge, United Kingdom, 2004.

    Google Scholar 

  19. R.P.W. Duin, E. Pękalska, and D.M.J. Tax. The characterization of classification problems by classifier disagreements. In International Conference on Pattern Recognition, volume 2, pages 140-143, Cambridge, United Kingdom, 2004.

    Google Scholar 

  20. R.P.W. Duin, F. Roli, and D. de Ridder. A note on core research issues for statistical pattern recognition. Pattern Recognition Letters, 23(4):493-499,2002.

    Article  MATH  Google Scholar 

  21. S. Edelman. Representation and Recognition in Vision. MIT Press, Cambridge, 1999.

    Google Scholar 

  22. B. Efron and R.J. Tibshirani. An Introduction to the Bootstrap. Chapman & Hall, London, 1993.

    MATH  Google Scholar 

  23. P. Flach and A. Kakas, editors. Abduction and Induction: essays on their relation and integration. Kluwer Academic Publishers, 2000.

    Google Scholar 

  24. A. Fred and A.K. Jain. Data clustering using evidence accumulation. In International Conference on Pattern Recognition, pages 276-280, Quebec City, Canada, 2002.

    Google Scholar 

  25. A. Fred and A.K. Jain. Robust data clustering. In Conf. on Computer Vision and Pattern Recognition, pages 442 -451, Madison - Wisconsin, USA, 2002.

    Google Scholar 

  26. K.S. Fu. Syntactic Pattern Recognition and Applications. Prentice-Hall, 1982.

    Google Scholar 

  27. K. Fukunaga. Introduction to Statistical Pattern Recognition. Academic Press, 1990.

    Google Scholar 

  28. G.M. Fung and O.L. Mangasarian. A Feature Selection Newton Method for Support Vector Machine Classification. Computational Optimization and Aplications, 28(2):185-202, 2004.

    Article  MATH  MathSciNet  Google Scholar 

  29. L. Goldfarb. On the foundations of intelligent processes - I. An evolving model for pattern recognition. Pattern Recognition, 23(6):595-616, 1990.

    Article  MathSciNet  Google Scholar 

  30. L. Goldfarb, J. Abela, V.C. Bhavsar, and V.N. Kamat. Can a vector space based learning model discover inductive class generalization in a symbolic environment? Pattern Recognition Letters, 16(7):719-726, 1995.

    Article  Google Scholar 

  31. L. Goldfarb and D. Gay. What is a structural representation? Fifth variation. Technical Report TR05-175, University of New Brunswick, Fredericton, Canada, 2005.

    Google Scholar 

  32. L. Goldfarb and O. Golubitsky. What is a structural measurement process? Technical Report TR01-147, University of New Brunswick, Fredericton, Canada, 2001.

    Google Scholar 

  33. L. Goldfarb and J. Hook. Why classical models for pattern recognition are not pattern recognition models. In International Conference on Advances in Pattern Recognition, pages 405-414, 1998.

    Google Scholar 

  34. T. Graepel, R. Herbrich, and K. Obermayer. Bayesian transduction. In Advances in Neural Information System Processing, pages 456-462, 2000.

    Google Scholar 

  35. T. Graepel, R. Herbrich, B. Schölkopf, A. Smola, P. Bartlett, K.-R. Müller, K. Obermayer, and R. Williamson. Classification on proximity data with LP-machines. In International Conference on Artificial Neural Networks, pages 304-309, 1999.

    Google Scholar 

  36. U. Grenander. Abstract Inference. John Wiley & Sons, Inc., 1981.

    Google Scholar 

  37. P. Grünwald, I.J. Myung, and Pitt M., editors. Advances in Minimum Description Length: Theory and Applications. MIT Press, 2005.

    Google Scholar 

  38. B. Haasdonk. Feature space interpretation of SVMs with indefinite kernels. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(5):482-492, 2005.

    Article  Google Scholar 

  39. I. Hacking. The emergence of probability. Cambridge University Press, 1974.

    Google Scholar 

  40. G. Harman and S. Kulkarni. Reliable Reasoning: Induction and Statistical Learning Theory. MIT Press, to appear.

    Google Scholar 

  41. S. Haykin. Neural Networks, a Comprehensive Foundation, second edition. Prentice-Hall, 1999.

    Google Scholar 

  42. D. Heckerman. A tutorial on learning with Bayesian networks. In M. Jordan, editor, Learning in Graphical Models, pages 301-354. MIT Press, Cambridge, MA, 1999.

    Google Scholar 

  43. T.K. Ho and M. Basu. Complexity measures of supervised classification problems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(3):289-300, 2002.

    Article  Google Scholar 

  44. A. K. Jain and B. Chandrasekaran. Dimensionality and sample size considerations in pattern recognition practice. In P. R. Krishnaiah and L. N. Kanal, editors, Handbook of Statistics, volume 2, pages 835-855. NorthHolland, Amsterdam, 1987.

    Google Scholar 

  45. A.K. Jain, R.P.W. Duin, and J. Mao. Statistical pattern recognition: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1):4-37, 2000.

    Article  Google Scholar 

  46. T. Joachims. Transductive inference for text classification using support vector machines. In I. Bratko and S. Dzeroski, editors, International Conference on Machine Learning, pages 200-209, 1999.

    Google Scholar 

  47. T. Joachims. Transductive learning via spectral graph partitioning. In International Conference on Machine Learning, 2003.

    Google Scholar 

  48. T.S. Kuhn. The Structure of Scientific Revolutions. University of Chicago Press, 1970.

    Google Scholar 

  49. L.I. Kuncheva. Combining Pattern Classifiers. Methods and Algorithms. Wiley, 2004.

    Google Scholar 

  50. J. Laub and K.-R. Müller. Feature discovery in non-metric pairwise data. Journal of Machine Learning Research, pages 801-818, 2004.

    Google Scholar 

  51. A. Marzal and E. Vidal. Computation of normalized edit distance and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(9):926-932, 1993.

    Article  Google Scholar 

  52. R.S. Michalski. Inferential theory of learning as a conceptual basis for multistrategy learning. Machine Learning, 11:111-151, 1993.

    MathSciNet  Google Scholar 

  53. T. Mitchell. Machine Learning. McGraw Hill, 1997.

    Google Scholar 

  54. Richard E. Neapolitan. Probabilistic reasoning in expert systems: theory and algorithms. John Wiley & Sons, Inc., New York, NY, USA, 1990.

    Google Scholar 

  55. C.S. Ong, S. Mary, X.and Canu, and Smola A.J. Learning with non-positive kernels. In International Conference on Machine Learning, pages 639-646, 2004.

    Google Scholar 

  56. E. Pękalska and R.P.W. Duin. The Dissimilarity Representation for Pattern Recognition. Foundations and Applications. World Scientific, Singapore, 2005.

    MATH  Google Scholar 

  57. E. Pękalska, R.P.W. Duin, S. Günter, and H. Bunke. On not making dissimilarities Euclidean. In Joint IAPR International Workshops on SSPR and SPR, pages 1145-1154. Springer-Verlag, 2004.

    Google Scholar 

  58. E. Pękalska, P. Paclík , and R.P.W. Duin. A Generalized Kernel Approach to Dissimilarity Based Classification. Journal of Machine Learning Research, 2:175-211, 2002.

    Article  MATH  Google Scholar 

  59. E. Pękalska, M. Skurichina, and R.P.W. Duin. Combining Dissimilarity Representations in One-class Classifier Problems. In Multiple Classifier Systems, pages 122-133. Springer-Verlag, 2004.

    Google Scholar 

  60. L.I. Perlovsky. Conundrum of combinatorial complexity. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(6):666-670, 1998.

    Article  Google Scholar 

  61. P. Pudil, J. Novovićova, and J. Kittler. Floating search methods in feature selection. Pattern Recognition Letters, 15(11):1119-1125, 1994.

    Article  Google Scholar 

  62. B. Ripley. Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge, 1996.

    MATH  Google Scholar 

  63. C.P. Robert. The Bayesian Choice. Springer-Verlag, New York, 2001.

    MATH  Google Scholar 

  64. K.M. Sayre. Recognition, a study in the philosophy of artificial intelligence. University of Notre Dame Press, 1965.

    Google Scholar 

  65. M.I. Schlesinger and Hlavác. Ten Lectures on Statistical and Structural Pattern Recognition. Kluwer Academic Publishers, 2002.

    Google Scholar 

  66. B. Schölkopf and A.J. Smola. Learning with Kernels. MIT Press, Cambridge, 2002.

    Google Scholar 

  67. J. Shawe-Taylor and N. Cristianini. Kernel methods for pattern analysis. Cambridge University Press, UK, 2004.

    Google Scholar 

  68. M. Stone. Cross-validation: A review. Mathematics, Operations and Statistics, (9):127-140, 1978.

    MATH  Google Scholar 

  69. D.M.J. Tax. One-class classification. Concept-learning in the absence of counter-examples. PhD thesis, Delft University of Technology, The Netherlands, 2001.

    Google Scholar 

  70. D.M.J. Tax and R.P.W. Duin. Support vector data description. Machine Learning, 54(1):45-56, 2004.

    Article  MATH  Google Scholar 

  71. F. van der Heiden, R.P.W. Duin, D. de Ridder, and D.M.J. Tax. Classification, Parameter Estimation, State Estimation: An Engineering Approach Using MatLab. Wiley, New York, 2004.

    Book  Google Scholar 

  72. V. Vapnik. Estimation of Dependences based on Empirical Data. Springer Verlag, 1982.

    Google Scholar 

  73. V. Vapnik. Statistical Learning Theory. John Wiley & Sons, Inc., 1998.

    Google Scholar 

  74. L.-X. Wang and J.M. Mendel. Generating fuzzy rules by learning from examples. IEEE Transactions on Systems, Man, and Cybernetics, 22(6):1414-1427, 1992.

    Article  MathSciNet  Google Scholar 

  75. S. Watanabe. Pattern Recognition, Human and Mechanical. John Wiley & Sons, 1985.

    Google Scholar 

  76. A. Webb. Statistical Pattern Recognition. John Wiley & Sons, Ltd., 2002.

    Google Scholar 

  77. S.M. Weiss and C.A. Kulikowski. Computer Systems That Learn. Morgan Kaufmann, 1991.

    Google Scholar 

  78. R.C. Wilson and E.R. Hancock. Structural matching by discrete relaxation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(6):634-648, 1997.

    Article  Google Scholar 

  79. R.C. Wilson, B. Luo, and E.R. Hancock. Pattern vectors from algebraic graph theory. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(7):1112-1124, 2005.

    Article  Google Scholar 

  80. S. Wolfram. A new kind of science. Wolfram Media, 2002.

    Google Scholar 

  81. D.H. Wolpert. The Mathematics of Generalization. Addison-Wesley, 1995.

    Google Scholar 

  82. R.R. Yager, M. Fedrizzi, and J. (Eds) Kacprzyk. Advances in the Dempster-Shafer Theory of Evidence. Wesley, 1994.

    Google Scholar 

  83. C.H. Yu. Quantitative methodology in the perspectives of abduction, deduction, and induction. In Annual Meeting of American Educational Research Association, San Francisco, CA, 2006.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. ICT group, Faculty of Electr. Eng., Mathematics and Computer Science, Delft University of Technology, The Netherlands

    Robert P. W. Duin

  2. School of Computer Science, University of Manchester, UK

    Elżbieta Pekalska

Authors
  1. Robert P. W. Duin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Elżbieta Pekalska
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Informatics, Nicolaus Copernicus University, Ul. Grudziadzka 5, 87-100, Torun, Poland

    Włodzisław Duch

  2. Division of Computer Science, School of Computer Engineering, Nanyang Technological University, 639798, Singapore

    Włodzisław Duch

  3. Faculty of Mathematics and Information Sciences, Warsaw University of Technology, Plac Politechniki 1, 00-661, Warsaw, Poland

    Jacek Mańdziuk

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Duin, R.P.W., Pekalska, E. (2007). The Science of Pattern Recognition. Achievements and Perspectives. In: Duch, W., Mańdziuk, J. (eds) Challenges for Computational Intelligence. Studies in Computational Intelligence, vol 63. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71984-7_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-71984-7_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71983-0

  • Online ISBN: 978-3-540-71984-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation