No, it does encompass GLM but the technique also works when there is no response (you then need to put a constraints on the parameter) or with truly non linear models with time series examples in the book. Or for particular clustering cases. I like to call it unsupervised regression, but a particular case with appropriate constraint on the parameters corresponds to classic regression. More about it here. As for shape classification, see here.
The problem is using Google to find anything other than stuff for the general public: you will get crap, and only crap. You are better off using the Reddit or StackOverflow search box. Or google "machine learning Quora" or "machine learning reddit".
Feel free to check out my own blog here, and I hope that you can find the high quality you are looking for (at least, things that make sense, and a lot of originality).
MLRecipes OP t1_j7oec5y wrote
Reply to comment by thiru_2718 in [N] New Book on Synthetic Data: Version 3.0 Just Released by MLRecipes
No, it does encompass GLM but the technique also works when there is no response (you then need to put a constraints on the parameter) or with truly non linear models with time series examples in the book. Or for particular clustering cases. I like to call it unsupervised regression, but a particular case with appropriate constraint on the parameters corresponds to classic regression. More about it here. As for shape classification, see here.