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Statistical learning theory is often associated with the principle of Occam's razor, which recommends a simplicity preference in inductive inference.
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Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition
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Trial and error predicates and the solution to a problem of Mostowski
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Language identification in the limit
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Occam’s razor
A. Blumer, A. Ehrenfeucht, D. Haussler, and M. K. Warmuth · 1987
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Learning from noisy examples
D. Angluin and P. Laird · 1988
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Learnability and the Vapnik-Chervonenkis dimension
A. Blumer, A. Ehrenfeucht, D. Haussler, and M. K. Warmuth · 1989
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Laws and Symmetry
B. C. van Fraassen · 1989
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Inductive learning by machines
S. Russell · 1991
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Computational Learning Theory , volume 30 of
M. Anthony and N. Biggs · 1992
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How tight are the Vapnik-Chervonenkis bounds?
D. Cohn and G. Tesauro · 1992
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On the connection between in-sample testing and generalization error
D. H. Wolpert · 1992
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Overfitting avoidance as bias
C. Schaffer · 1993
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How to tell when simpler, more unified, or less
M. R. Forster and E. Sober · 1994
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An Introduction to Computational Learning Theory
M. J. Kearns and U. V. Vazirani · 1994
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A conservation law for generalization performance
C. Schaffer · 1994
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A Probabilistic Theory of Pattern Recognition , volume 31 of
L. Devroye, L. Györfi, and G. Lugosi · 1996
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The Logic of Reliable Inquiry
K. T. Kelly · 1996
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Further experimental evidence against the utility of Occam’s razor
G. I. Webb · 1996
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The lack of a priori distinctions between learning algorithms
D. H. Wolpert · 1996
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Machine Learning
T. M. Mitchell · 1997
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Occam’s two razors: The sharp and the blunt
P. Domingos · 1998
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Pragmatism and change of view
I. Levi · 1998
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Statistical Learning Theory
V. N. Vapnik · 1998
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The role of Occam’s razor in knowledge discovery
P. Domingos · 1999
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Systems That Learn: An Introduction to Learning Theory
S. Jain, D. N. Osherson, J. S. Royer, and A. Sharma · 1999
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Means-ends epistemology
O. Schulte · 1999
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An overview of statistical learning theory
V. N. Vapnik · 1999
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A few useful things to know about machine learning
P. Domingos · 2012
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Understanding Machine Learning: From Theory to Algorithms
S. Shalev-Shwartz and S. Ben-David · 2014
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Fast rates in statistical and online learning
T. van Erven, P. D. Grünwald, N. A. Mehta, M. D. Reid, and R. C. Williamson · 2015
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Ockham’s Razors: A User’s Manual
E. Sober · 2015
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Deep Learning
I. Goodfellow, Y. Bengio, and A. Courville · 2016
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Learning theory and epistemology
K. T. Kelly · 2016
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Solomonoff prediction and Occam’s razor
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Hume’s Problem: Induction and the Justification of Belief
C. Howson · 2000
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The false hopes of traditional epistemology
B. C. van Fraassen · 2000
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The Nature of Statistical Learning Theory
V. N. Vapnik · 2000
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Statistical modeling: The two cultures (with comments and a rejoinder by the author)
L. Breiman · 2001
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Pattern Classification
R. O. Duda, P. E. Hart, and D. G. Stork · 2001
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T. F. Sterkenburg · 2016
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Inherent complexity: A problem for statistical model evaluation
J.-W. Romeijn · 2017
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Formal Learning Theory
O. Schulte · 2017
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The Topology of Statistical Inquiry
K. Genin · 2018
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Foundations of Machine Learning
M. Mohri, A. Rostamizadeh, and A. Talwalkar · 2018
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Universal Prediction: A Philosophical Investigation
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On learnability wih computable learners
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Introduction to Machine Learning
E. Alpaydin · 2020
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PAC learning and Occam’s razor: Probably approximately incorrect
D. A. Herrmann · 2020
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Deep learning: A statistical viewpoint
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Fit without fear: Remarkable mathematical phenomena of deep learning through the prism of interpolation
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A theory of universal learning
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The no-free-lunch theorems of supervised learning
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Simplicity
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Simple models in complex worlds: Occam’s razor and statistical learning theory
F. J. Bargagli Stoffi, G. Cevolani, and G. Gnecco · 2022
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Philosophy of science at sea: Clarifying the interpretability of machine learning
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The modern mathematics of deep learning
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Patterns, Predictions, and Actions: Foundations of Machine Learning
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On characterizations of learnability with computable learners
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A fine-grained analysis on distribution shift
O. Wiles, S. Gowal, F. Stimberg, S. Rebuffi, I. Ktena, K. Dvijotham, and A. T. Cemgil · 2022
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Functionspaces, simplicity and curve fitting
T. Bonk · 2023
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