PhilSci Archive

A statistical learning approach to a problem of induction

Zhao, Kino (2018) A statistical learning approach to a problem of induction. In: UNSPECIFIED.

This is the latest version of this item.

[img]
Preview
Text
Statistical Learning Theory.pdf

Download (270kB) | Preview

Abstract

At its strongest, Hume's problem of induction denies the existence of any well justified assumptionless inductive inference rule. At the weakest, it challenges our ability to articulate and apply good inductive inference rules. This paper examines an analysis that is closer to the latter camp. It reviews one answer to this problem drawn from the VC theorem in statistical learning theory and argues for its inadequacy. In particular, I show that it cannot be computed, in general, whether we are in a situation where the VC theorem can be applied for the purpose we want it to.


Export/Citation: EndNote | BibTeX | Dublin Core | ASCII/Text Citation (Chicago) | HTML Citation | OpenURL
Social Networking:
Share |

Item Type: Conference or Workshop Item (UNSPECIFIED)
Creators:
CreatorsEmailORCID
Zhao, Kinoyutingz3@uci.edu
Keywords: statistical learning theory, problem of induction, model theory
Subjects: Specific Sciences > Mathematics > Logic
General Issues > Confirmation/Induction
General Issues > Formal Learning Theory
Depositing User: Dr. Kino Zhao
Date Deposited: 08 Dec 2018 17:28
Last Modified: 08 Dec 2018 17:28
Item ID: 15422
Subjects: Specific Sciences > Mathematics > Logic
General Issues > Confirmation/Induction
General Issues > Formal Learning Theory
Date: November 2018
URI: https://philsci-archive.pitt.edu/id/eprint/15422

Available Versions of this Item

  • A statistical learning approach to a problem of induction. (deposited 08 Dec 2018 17:28) [Currently Displayed]

Monthly Views for the past 3 years

Monthly Downloads for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item