Rethinking statistical learning theory: learning using statistical invariantsVapnik
New paper:
Rethinking statistical learning theory: learning using statistical invariants Vladimir Vapnik and Rauf Izmailov https://link.springer.com/article/10...99401857420 After doing the Edx course and reading the book and echapters, I was of course excited to find Vapnik's possibly important new improved approach to ML. The two main ideas, which I believe are new: 1) vSVM  the SVM but with Vmatrix 2) Using Statistical Invariants to improve convergence without extra training samples. LUSI Learning Using Statistical Invariants. Here is the paper, which I have not read, because I can't get a copy that fits my budget (less that the $39 from Springer). Vapnik, V. & Izmailov, R. Mach Learn (2018). https://doi.org/10.1007/s1099401857420 However, Vapnik has given at least three related lectures in late 2018 (including slides with complicated math), one of which is on youtube here: https://www.youtube.com/watch?v=rNd7PDdhl4c Most intriguing to me, is his comment suggesting that these new techniques are more powerful than deep neural networks. I didn't think that was currently possible in general, or at least not in certain domains, ie. image recognition etc. I'm probably missing something. Can anyone, or the authors :), comment in detail about how this paper fits into the framework of ideas presented in the book? 
Re: Rethinking statistical learning theory: learning using statistical invariantsVap
which text book does he talk about at 26:28 ?

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