Introduction: machine learning as philosophy of science

Authors:
Kevin B. Korb
Affiliations:
School of Computer Science and Software Engineering, Monash University, Clayton, Australia
Venue:
Minds and Machines - Machine learning as experimental philosophy of science
Year:
2004

Citing 6
Cited 1

Scientific discovery: computational explorations of the creative process

Scientific discovery: computational explorations of the creative process
Computational philosophy of science

Computational philosophy of science
What computers still can't do: a critique of artificial reason

What computers still can't do: a critique of artificial reason
A pragmatic Bayesian platform for automating scientific induction

A pragmatic Bayesian platform for automating scientific induction
Bayesian Artificial Intelligence

Bayesian Artificial Intelligence
A study of causal discovery with weak links and small samples

IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2

An Object-Oriented View on Problem Representation as a Search-Efficiency Facet: Minds vs. Machines

Minds and Machines

Quantified Score

Hi-index	0.00

Visualization

Abstract

I consider three aspects in which machine learning and philosophy of science can illuminate each other: methodology, inductive simplicity and theoretical terms. I examine the relations between the two subjects and conclude by claiming these relations to be very close.