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Structures and Features of Optimal Information Systems

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Information Systems and Data Compression
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Abstract

The first purpose of this chapter is to present a systematic approach to the optimization problems. In the preceding chapters we considered several concrete optimization problems. They now serve as examples of general methods that are presented here. On the other hand, considerations in this chapter show those specific problems in a broader perspective and provide formal justifications for the heuristic assumption that have been introduced previously.

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References

  1. Makhoul, J., Roucos, S., Gish, H., Vector Quantization in Speech Coding, Procc. IEEE, vol 73 (1985) pp. 1551–1588.

    Article  Google Scholar 

  2. Lim, J.S., Two-Dimensional Signal and Image Processing, Prentice Hall, Englewood Cliffs, NJ, 1990.

    Google Scholar 

  3. Daugherty, E.R., Digital Image Processing Methods, Marcel Dekker, NY, 1994.

    Google Scholar 

  4. Russ, J.C., ed., The Image Processing Handbook, (2-nd ed.), IEEE Press, NY, 1994.

    Google Scholar 

  5. Minoux, M., Mathematical Programming: Theory and Algorithms, J. Wiley, NY, 1986.

    MATH  Google Scholar 

  6. Cuthbert, T.R., Optimization Using Personal Computers, J. Wiley, NY, 1986.

    Google Scholar 

  7. Press, W.H., Flannery, B.P., Teukolsy, S.A., Vetterling, W.T., Numerical Recipes, Cambridge University Press, Cambridge, 1992.

    Google Scholar 

  8. Thompson, E.E., An Introduction to Algebra of Matrices with some Applications, Adam Hilger, London, 1969.

    MATH  Google Scholar 

  9. Middleton, R.H., Goodwin, G.c., Digital Control and Estimation: A Unified Approach, Prentice Hall, Englewood Cliffs, NJ, 1990.

    MATH  Google Scholar 

  10. Schmetterer, L., Stochastic Approximations, Procc. of 4-th Berkley Symposium on Mathematical Statistics, vol 9, (1961), pp. 587–609.

    MathSciNet  Google Scholar 

  11. Tsypkin Ya.,Z., Foundations of Theory of Learning Systems, Academic Press, N.Y., 1973.

    MATH  Google Scholar 

  12. Widrow B., Stearns S.,D., Adaptive Signal Processing, Prentice Hall, Englewood Cliffs, 1985.

    MATH  Google Scholar 

  13. Proakis, J.G., Digital Communications, (2nd ed.), McGraw-Hill, NY, 1989.

    Google Scholar 

  14. Zurada, J.M., Introduction to Artificial Neural Systems, West, St.Poul, MN, 1992.

    Google Scholar 

  15. Haykin, S., Neural Networks: A Comprehensive Foundation, IEEE Press, NY, 1994.

    MATH  Google Scholar 

  16. Goldberg, D.E., Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Weseley, NY, 1989.

    MATH  Google Scholar 

  17. Soucek, B. and IRIS Group, Dynamic, Genetic, and Chaotic Programming, J. Wiley, NY, 1992.

    MATH  Google Scholar 

  18. Schalkwijk, J.P.M., Center of Gravity Information Feedback, IEEE Trans. On Information Theory, vol IT-14 (1968), pp.324–331.

    Article  MathSciNet  Google Scholar 

  19. Papoulis, R., Probability, Random Variables, and Stochastic Processes, McGraw-Hill, NY, 1991.

    Google Scholar 

  20. Blachut, R.E., Principles and Practice in Information Theory, Addison-Wesley, Reading, MA, 1990.

    Google Scholar 

  21. Cover, T.M., Elements of Information Theory, J. Wiley, NY, 1991.

    MATH  Google Scholar 

  22. Golomb, S.W., Peile R.A., Scholtz, R.A., Basic Concepts in Information Theory and Coding, Plenum Press, NY, 1994.

    MATH  Google Scholar 

  23. Seidler, J.A., Digital Data Transmission Systems, (in Polish), W.N.T., Warsaw, 1976; (in Russian) SWAZ, Moscow, 1978.

    Google Scholar 

  24. Kleinrock, L., Queuing Systems (2 vols.), J. Wiley, NY., 1976.

    Google Scholar 

  25. Seidler, J.A., Principles of Computer Communication Network Design, J. Wiley, NY, 1983.

    Google Scholar 

  26. Bertsekas, D., Gallager R., Data Networks, Prentice Hall, Englewood Cliffs, NJ, 1987.

    Google Scholar 

  27. Judell, N., Scharf, L., A Simple Derivation of Lloyd’s Classical Result for the Optimal Scalar Quantizer, IEEE Trans.on Information Theory, vol 32 (1986), pp.326–328.

    Article  Google Scholar 

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© 1997 Kluwer Academic Publishers

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(1997). Structures and Features of Optimal Information Systems. In: Information Systems and Data Compression. Springer, Boston, MA. https://doi.org/10.1007/978-0-585-27999-2_8

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  • DOI: https://doi.org/10.1007/978-0-585-27999-2_8

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-7923-9953-7

  • Online ISBN: 978-0-585-27999-2

  • eBook Packages: Springer Book Archive

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