文件名称:test
- 所属分类：
- matlab例程
- 资源属性：
- [Matlab] [源码]
- 上传时间：
- 2012-11-26
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- 35kb
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- 0次
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- Ayike ******
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function mean = ml_mean(data)
Calculate the maximum likelihood estimation of the mean
Written by Christiaan M. van der Walt
Meraka Institute
More resources available at http://www.patternrecognition.co.za
Reference:
C.M. van der Walt and E. Barnard,“Data characteristics that determine classifier perfromance”, in Proceedings
of the Sixteenth Annual Symposium of the Pattern Recognition Association of South Africa, pp.160-165, 2006.
Available [Online] http://www.patternrecognition.co.za-function mean = ml_mean(data)
Calculate the maximum likelihood estimation of the mean
Written by Christiaan M. van der Walt
Meraka Institute
More resources available at http://www.patternrecognition.co.za
Reference:
C.M. van der Walt and E. Barnard,“Data characteristics that determine classifier perfromance”, in Proceedings
of the Sixteenth Annual Symposium of the Pattern Recognition Association of South Africa, pp.160-165, 2006.
Available [Online] http://www.patternrecognition.co.za
Calculate the maximum likelihood estimation of the mean
Written by Christiaan M. van der Walt
Meraka Institute
More resources available at http://www.patternrecognition.co.za
Reference:
C.M. van der Walt and E. Barnard,“Data characteristics that determine classifier perfromance”, in Proceedings
of the Sixteenth Annual Symposium of the Pattern Recognition Association of South Africa, pp.160-165, 2006.
Available [Online] http://www.patternrecognition.co.za-function mean = ml_mean(data)
Calculate the maximum likelihood estimation of the mean
Written by Christiaan M. van der Walt
Meraka Institute
More resources available at http://www.patternrecognition.co.za
Reference:
C.M. van der Walt and E. Barnard,“Data characteristics that determine classifier perfromance”, in Proceedings
of the Sixteenth Annual Symposium of the Pattern Recognition Association of South Africa, pp.160-165, 2006.
Available [Online] http://www.patternrecognition.co.za
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下载文件列表
test\get_clmeans.m
....\get_clvar.m
....\iris.txt
....\iyon.txt
....\ml_cov.m
....\ml_mean.m
....\mvgauss.m
....\nbclassifier.asv
....\nbclassifier.m
....\get_clvar.m
....\iris.txt
....\iyon.txt
....\ml_cov.m
....\ml_mean.m
....\mvgauss.m
....\nbclassifier.asv
....\nbclassifier.m