On 9/7/12 2:22 PM, Ted Dunning wrote: > This is great. > > A very useful feature would be to allow basic L_1 and L_2 regularization. > > This makes it much easier to avoid problems with separable problems. > > It might be interesting to think for a moment how easy it would be to > support generalized linear regression in this same package. Small changes > to the loss function in the optimization should allow you to have not just > logistic and probit regression, but also to get Poisson regression and SVM > in the same framework.
+1 Patches welcome! Phil > > On Fri, Sep 7, 2012 at 3:22 AM, marios michaelidis > <mimari...@hotmail.com>wrote: > >> I am willing to provide complete >> Logistic and Probit regression algorithms, optimizable by newton Raphson >> optimization maximum-likelihood method , in a very programmatically easy >> way >> (e.g regression(double matrix [][], double Target[], String >> Constant, double precision, double tolerance) , with academic references >> and >> very quick (3 secs for 60k set), with getter methods for all the common >> statistics such as null Deviance, Deviance, AIC, BIC, Chi-square f the >> model, >> betas, Wald statistics and p values, Cox_snell R square, Nagelkerke’s >> R-Square, >> Pseudo_r2, residuals, probabilities, classification matrix. >> --------------------------------------------------------------------- To unsubscribe, e-mail: dev-unsubscr...@commons.apache.org For additional commands, e-mail: dev-h...@commons.apache.org
