Using Genetic Algorithm for Parameter Estimation
By Yi Wang
Computer Science Department,Tsinghua University,100084, Beijing, China
wangy01@mails.tsinghua.edu.cn ...
Parameter Estimation Density estimation when the density is assumed to be in a specific parametric family. Special cases include maximum likelihood, maximum a posteriori, unbiased estimation, and predictive estimation.
Parameter Estimation
There are several different methods for estimating the parameters. All of them should produce very similar estimates, but may be more or less efficient for any given model.
[edit] Parameter Estimation
Every data mining task has the problem of parameters. Every parameter influences the algorithm in specific ways. For DBSCAN the parameters epsilon and MinPnts are needed.
Parameter Estimation
Since the chi-square distribution is typically used to develop hypothesis tests and confidence intervals and rarely for modeling applications, we omit any discussion of parameter estimation.
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Parameter Estimation
Subset Selection
Sequencing
3.2 Parameter Estimation ...
The two main parameter estimation techniques are :
Maximizing the Likelihood of the sample,
Minimizing the sum of the squares of the errors of the model predictions on the design set (or training set).
 See also: Estimation, Distribution, Density, Likelihood, Distribution function
  
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