Interesting papers related to my recent work and
both from http://www.princeton.edu/~harman/Papers/
Title:
Statistical Learning Theory as a Framework for the Philosophy of Induction
Link:
http://www.princeton.edu/~harman/Papers/Induction-JH.pdf
Abstract:
Statistical Learning Theory (e.g., Hastie et al., 2001; Vapnik, 1998, 2000,
2006) is the basic theory
behind contemporary machine learning and data-mining. We suggest that the
theory provides an
excellent framework for philosophical thinking about inductive inference.1
We begin by sketching certain aspects of statistical learning theory, then
comment briefly on philosophical
implications for reliability theories of justification, Karl Popper’s (1979,
2002) appeal to
falsifiability, simplicity as relevant to inductive inference, and whether
direct induction is an instance
of inference to the best explanation.
Title:
Reliable Reasoning: Induction and Statistical Learning Theory
Link:
http://www.princeton.edu/~kulkarni/Papers/b2006_hk.pdf
Abstract:
The book arises from an introductory course on Learning Theory and
Epistemology we have been teaching jointly in Electrical Engineering and
Philosophy at Princeton University. This low-level undergraduate course
serves as an introduction to aspects of philosophy, computer science,
engineering,
statistics and cognitive science. It is open to all students and has
no specific prerequisites other than some analytical skills and intellectual
curiosity. Although much of the material is technical, we have found that
the main points are both accessible to and appreciated by a broad range
of students. In each class, our students have included freshmen through
seniors, with majors from the sciences, engineering, humanities, and social
sciences. We acknowledge with thanks a Curriculum Development Grant
for this course from the 250th Anniversary Fund for Innovation in
Undergraduate
Education from Princeton University and are grateful to the many
students who have discussed the content of the course with us.
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