Regression equation - page 16
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How long did it take you to make this discovery? :)
For example, don't you want to open something like "matrices in currency analysis"?
For example, do you by any chance want to start something like "matrices in currency analysis"?
I've had enough of my labs, thank you.
They've made me sleep every other day for the second week in a row.
I've had enough of my labs, thank you.
They've made me sleep every other night for the second week.
Spit it out. There's a catastrophic lack of good stuff.
P.S. Of course, you're bound to get pooped on. But you'll stop paying attention at some point.
Spit it out. There is catastrophically little that is useful.
P.S. Of course, you're bound to get pooped on. But you'll stop paying attention to that at some point.
)) So it's probably about university :)
Regarding the article - I have seen somewhere an implementation (or similar) of the algorithm (by these authors). As soon as I find it, I'll post it.
P.S. I don't have the full article. ((
If anyone still has interest, the second author of the article in the introduction to his PhD in Economics (2006, Muravyev, Dmitry Georgievich, Mathematical and Instrumental Methods in Economics, Scientific Library of Dissertations and Author's Abstracts dissertation dissertationCat http://www.dissercat.com/content/matematicheskie-metody-razrabotki-i-otsenki-strategii-torgovli-na-mezhbankovskom-valyutnom-r?_openstat=cmVmZXJ1bi5jb207bm9kZTthZDE7#ixzz3vXr6iRi5) notes:
"The methods and algorithms developed in this paper are based on V.N. Vapnik's ideas of finding a rule close to best in class for a given sample size with estimation of rule quality on the general population with a given reliability."
Vapnik has been involved in pattern recognition for decades, and as applied to said "rule finding" he wrote a very good monograph
Vapnik V. N. Dependence reconstruction from empirical data.-Moscow: Nauka, 1979. - 448 p. http://www. machinelearning.ru/wiki/index.php?title=%D0%9F%D1%83%D0%B1%D0%BB%D0%B8%D0%BA%D0%B0%D1%86%D0%B8%D1%8F:%D0%92%D0%B0%D0%BF%D0%BD%D0%B8%D0%BA_1979_%D0%92%D0%BE%D1%81%D1%81%D1%82%D0%B0%D0%BD%D0%BE%D0%B2%D0%BB%D0%B5%D0%BD%D0%B8%D0%B5_%D0%B7%D0%B0%D0%B2%D0%B8%D1%81%D0%B8%D0%BC%D0%BE%D1%81%D1%82%D0%B5%D0%B9
The notion of average risk, or empirical risk, is introduced, which includes not only the risk of deviation of the approximating function from the available data (it is minimized by OLS) but also the risk of excessive number of fitted parameters or functions.
I used, as I recall, his other book, 1984, Algorithms and Dependency Recovery Programs, which allowed me to write an implementation directly from the book's text in Fortran. I took from different places point-defined functions, calculated approximations by algebraic and trigonometric polynomials, mixed combinations of any functions at all. I was amazed at how precisely his algorithms determined how many parameters to keep and how many will be unnecessary. I was surprised in the sense that in almost all examples I myself would leave the same amount and the same parameters.