Page 28 - Introduction to Statistical Pattern Recognition

10 Introduction to Statistical Pattern Recognition

M =E { X ) = EL PiM; Expected vector of the mixture
r=l
density
Zi =E {(X -M;)(X --Mi)‘ I O; } Covariance matrix of O;

Z =E{ (X -M)(X - M)’}
(PJ, +P,(M, -M)(M; -M)‘ 1 Covariance matrix of the
r=l
mixture density

References

1. K. Fukunaga, “Introduction to Statistical Pattern Recognition,” Academic
Press, New York, 1972.

2. R. 0. Duda and P. E. Hart, “Pattern Classification and Scene Analysis,”
Wiley, New York, 1973.
3. P. R. Devijver and J. Kittler, “Pattern Recognition: A Statistical
Approach,” Prentice-Hall, Englewood Cliffs, New Jersey, 1982.
4. A. K. Agrawala (ed.), “Machine Recognition of Patterns,” IEEE Press,
New York, 1977.
5. L. N. Kanal, Patterns in pattern recognition: 1968-1972, Trans. IEEE
Inform. Theory, IT-20, pp. 697-722,1974.
6. P. R. Krishnaiah and L. N. Kanal (eds.), “Handbook of Statistics 2:
Classification, Pattern Recognition and Reduction of Dimensionality,”
North-Holland, Amsterdam, 1982.
7. T. Y. Young and K. S. Fu (eds.), “Handbook of Pattern Recognition and
Image Processing,” Academic Press, New York, 1986.