Two of the best fundamental probability guys, Jaynes and Wolpert, both basically said that the applications of their theories to infinite sets were unnecessary and likely more trouble than they’re worth.
Amazon doesn’t provide an index, but the title was promising enough that I bought one. The date looks good too, as it was after I know much of the original papers were completed.
What you want are his general framework for analyzing generalization problems, and his application of that framework to Stacked Generalization and No Free Lunch Theorems in machine learning and Search/Optimization.
Sorry I don’t have better details, but the papers are in storage, and I read them 15+ years ago.
Two of the best fundamental probability guys, Jaynes and Wolpert, both basically said that the applications of their theories to infinite sets were unnecessary and likely more trouble than they’re worth.
Yeah I read PT:LOS and I’d like to be able to say that, but infinite ethics doesn’t really look to be so easily swept under the rug.
Would you recommend The Mathematics of Generalization by Wolpert, and/or something else?
Amazon doesn’t provide an index, but the title was promising enough that I bought one. The date looks good too, as it was after I know much of the original papers were completed.
What you want are his general framework for analyzing generalization problems, and his application of that framework to Stacked Generalization and No Free Lunch Theorems in machine learning and Search/Optimization.
Sorry I don’t have better details, but the papers are in storage, and I read them 15+ years ago.
Thanks anyway, I’ll look up the papers. :)