> Although I have come across the concept of kernel-based methods, I still > do not understand exactly what that means. I am an engineer who dabbles > in applied mathematics. I do not think the method I have implemented is > kernel-based. It is sometimes known as "penalized least squares"[1]. > The idea is to minimize a function that is the sum of a goodness of fit > term and a data roughness term. I have implemented it for > one-dimensional data only, and I have not yet seen a multi-dimensional > implementation in the literature, although it may be possible. >
Yes, I know what you mean - Tikhonov regularization (also ridge regression) sort-of corresponds to using "white noise" term in GPR. I thought there is something already for ridge regression in Octave-Forge, but I could not find anything. -- RNDr. Jaroslav Hajek computing expert Aeronautical Research and Test Institute (VZLU) Prague, Czech Republic url: www.highegg.matfyz.cz ------------------------------------------------------------------------- Check out the new SourceForge.net Marketplace. It's the best place to buy or sell services for just about anything Open Source. http://ad.doubleclick.net/clk;164216239;13503038;w?http://sf.net/marketplace _______________________________________________ Octave-dev mailing list Octave-dev@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/octave-dev
