The rudiments of modern theory can be understood with little more difficulty, and it is clear from those that any significant restriction of the number of degre

The rudiments of modern theory can be understood with little more difficulty, and it is clear from those that any significant restriction of the number of degrees of freedom of a model reduces the chances of overfitting occurring, but also decreases the fraction of predicitions the model will get right, so the real issue is where exactly do we draw a line, and this is now understood quite well - the approach from the paper, for practical purposes, throws out the baby with the bathwater. The first few lectures of the course of machine learning by Yaser Abu-Mostafa are a really engaging introduction to those topics: https://work.caltech.edu/ https://work.caltech.edu/ By the way Howard Wainer is a noted author of semi-popular (some formulas actually appear etc.) statistics books, so if enjoyed the writing, maybe a better use of time would be to read his newer and more general stuff: http://www.amazon.com/Howard-Wainer/e/B000AP7SUU/ref=sr_ntt_srch_lnk_1?qid=1401098828&sr=8-1 http://www.amazon.com/Howard-Wainer/e/B000AP7SUU/ref=sr_ntt_...