Machine Learning and the foundations of inductive inference

Francesco Bergadano

doi:10.1007/bf00974304

Machine Learning and the foundations of inductive inference

Minds and Machines ◽

10.1007/bf00974304 ◽

1993 ◽

Vol 3 (1) ◽

pp. 31-51 ◽

Cited By ~ 1

Author(s):

Francesco Bergadano

Keyword(s):

Machine Learning ◽

Inductive Inference

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Connections Between Inductive Inference and Machine Learning

10.1007/springerreference_178872 ◽

2012 ◽

Keyword(s):

Machine Learning ◽

Inductive Inference

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SIZES OF ORDERED DECISION TREES

International Journal of Foundations of Computer Science ◽

10.1142/s0129054102001205 ◽

2002 ◽

Vol 13 (03) ◽

pp. 445-458 ◽

Cited By ~ 1

Author(s):

HANS ZANTEMA ◽

HANS L. BODLAENDER

Keyword(s):

Machine Learning ◽

Decision Tree ◽

Decision Trees ◽

Blow Up ◽

Inductive Inference ◽

Hard Problem ◽

Minimal Size ◽

Knowledge Based ◽

Np Hard Problem ◽

Decision Tables

Decision tables provide a natural framework for knowledge acquisition and representation in the area of knowledge based information systems. Decision trees provide a standard method for inductive inference in the area of machine learning. In this paper we show how decision tables can be considered as ordered decision trees: decision trees satisfying an ordering restriction on the nodes. Every decision tree can be represented by an equivalent ordered decision tree, but we show that doing so may exponentially blow up sizes, even if the choice of the order is left free. Our main result states that finding an ordered decision tree of minimal size that represents the same function as a given ordered decision tree is an NP-hard problem; in earlier work we obtained a similar result for unordered decision trees.

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Fundamental Mechanisms in Machine Learning and Inductive Inference

Fundamentals of Artificial Intelligence ◽

10.1007/978-3-662-40145-3_4 ◽

1987 ◽

pp. 133-169 ◽

Cited By ~ 1

Author(s):

Alan W. Biermann

Keyword(s):

Machine Learning ◽

Inductive Inference

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Connections Between Inductive Inference and Machine Learning

Encyclopedia of Machine Learning ◽

10.1007/978-0-387-30164-8_160 ◽

2011 ◽

pp. 210-219

Author(s):

Thomas R. Shultz ◽

Scott E. Fahlman ◽

Susan Craw ◽

Periklis Andritsos ◽

Panayiotis Tsaparas ◽

...

Keyword(s):

Machine Learning ◽

Inductive Inference

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Connections Between Inductive Inference and Machine Learning

Encyclopedia of Machine Learning and Data Mining ◽

10.1007/978-1-4899-7502-7_52-1 ◽

2016 ◽

pp. 1-11

Author(s):

John Case ◽

Sanjay Jain

Keyword(s):

Machine Learning ◽

Inductive Inference

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Aspects of Inductive Inference in Statistics and Machine Learning

Indian Statistical Institute Series - A Tribute to the Legend of Professor C. R. Rao ◽

10.1007/978-981-33-6991-7_10 ◽

2021 ◽

pp. 77-91

Author(s):

Palash Sarkar

Keyword(s):

Machine Learning ◽

Inductive Inference

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Fundamental mechanisms in machine learning and inductive inference: Part 2

Advanced Topics in Artificial Intelligence - Lecture Notes in Computer Science ◽

10.1007/3-540-50676-4_12 ◽

1988 ◽

pp. 125-145 ◽

Cited By ~ 1

Author(s):

Alan W. Biermann

Keyword(s):

Machine Learning ◽

Inductive Inference

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Fundamental mechanisms in machine learning and inductive inference

Fundamentals of Artificial Intelligence - Lecture Notes in Computer Science ◽

10.1007/bfb0022682 ◽

2005 ◽

pp. 133-169 ◽

Cited By ~ 2

Author(s):

Alan W. Biermann

Keyword(s):

Machine Learning ◽

Inductive Inference

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Connections Between Inductive Inference and Machine Learning

Encyclopedia of Machine Learning and Data Mining ◽

10.1007/978-1-4899-7687-1_52 ◽

2017 ◽

pp. 261-272

Author(s):

John Case ◽

Sanjay Jain

Keyword(s):

Machine Learning ◽

Inductive Inference

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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