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size, while we need spss estimate 2d+frac+2 parameters in LDA two mean values for spss Gaussians, spss d by d symmetric covariance matrices, and two priors for spss two classes and spss number of parameters grows quadratically w. r. t. measurement. Note that spss number of parameters also corresponds spss spss minimal variety of observations needed spss compute spss coefficients of every function. Techniques do exist though for handling high dimensional issues where spss number of parameters exceeds spss variety of observations. Logistic Regression can be changed using shrinkage methods spss handle spss challenge of having less observations than parameters. This resulting optimization problem is convex and can be solved using Newton Raphson method as given in Zhu and hastie 2004. LDA involves spss inversion of spss help d x d covariance matrix. When here is spss case, we can either use spss pseudo inverse or carry out regularized discriminant evaluation which solves this problem. Cross validation can be utilized spss calculate spss best , gamma.





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