Referenced Items are not available for this document.
Recent theoretical advances have shown the applicability of neural networks in density estimation. However, training in these methods is slow, especially where gaps exist in the data (which is often the case in practical situations). A standard method for attacking this missing data problem is to use the GEM (or generalized expectation-maximization) algorithm. We apply this algorithm to conditional density estimation with missing data, and show that it requires significantly fewer training examples to attain acceptable performance
Published in:
Circuits and Systems, 1996. ISCAS '96., Connecting the World., 1996 IEEE International Symposium on
(Volume:3
)
Date of Conference: 12-15 May 1996
- Page(s):
- 305 - 308 vol.3
- Meeting Date :
- 12 May 1996-15 May 1996
- Print ISBN:
- 0-7803-3073-0
- INSPEC Accession Number:
- 5443598
- Conference Location :
- Atlanta, GA
- Digital Object Identifier :
- 10.1109/ISCAS.1996.541594
- Product Type:
- Conference Publications
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