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- W89230552 abstract "Reliable Reasoning is a simple, accessible, beautifully explained introduction to Vapnik and Chervonenkis’s statistical learning theory. It includes a modest discussion of the application of the theory to the philosophy of induction; the purpose of these remarks is to say something more. 1. A Patient Pessimist’s Guide to Induction Philosophical Learning Theory Vapnik and Chervonenkis’s statistical learning theory may be compared to formal learning theory, familiar to philosophers from the work of Putnam (1963) and Kelly (1996). There are significant technical differences between the two theories, but considered as philosophical frameworks for thinking about inductive reasoning, they have much in common. I will say that they are both—in their epistemological incarnations—species of philosophical learning theory . The programmatic goal of formal learning theory is to investigate methods for learning from experience that are guaranteed to converge on the truth. (or at least guaranteed to come as close as possible) under some given set of circumstances. If you have a method that is sure to converge, the thought goes, then provided that the particular circumstances within which the guarantee is offered actually hold, you are sure to find the truth—eventually. The problem of induction is in that case solved. Or at least, a problem of induction is solved, since different methods may be recommended in different circumstances. Statistical learning theory takes a similar approach; the most important difference for philosophical purposes is that, assuming as it does that we live in an inherently stochastic world, it does not pursue convergence per se but a kind (or several kinds) of probabilistic convergence. Rather than providing a guarantee of convergence on the truth, it" @default.
- W89230552 created "2016-06-24" @default.
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- W89230552 date "2009-03-30" @default.
- W89230552 modified "2023-09-24" @default.
- W89230552 title "REMARKS ON HARMAN AND KULKARNI'S RELIABLE REASONING" @default.
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