Title
Mitotic Classes in Inductive Inference
Abstract
For the natural notion of splitting classes into two disjoint subclasses via a recursive classifier working on texts, the question of how these splittings can look in the case of learnable classes is addressed. Here the strength of the classes is compared using the strong and weak reducibility from intrinsic complexity. It is shown that, for explanatorily learnable classes, the complete classes are also mitotic with respect to weak and strong reducibility, respectively. But there is a weakly complete class that cannot be split into two classes which are of the same complexity with respect to strong reducibility. It is shown that, for complete classes for behaviorally correct learning, one-half of each splitting is complete for this learning notion as well. Furthermore, it is shown that explanatorily learnable and recursively enumerable classes always have a splitting into two incomparable classes; this gives an inductive inference counterpart of the Sacks splitting theorem from recursion theory.
Year
DOI
Venue
2008
10.1137/070700577
SIAM J. Comput.
Keywords
Field
DocType
learnable class,explanatorily learnable class,strong reducibility,weak reducibility,explanatorily learnable,splitting class,sacks splitting theorem,weakly complete class,complete class,behaviorally correct learning,inductive inference,mitotic classes,recursion theory,reducibilities
Inductive reasoning,Discrete mathematics,Combinatorics,Disjoint sets,Splitting theorem,Computability theory,Recursively enumerable language,Reduction (recursion theory),Classifier (linguistics),Mathematics,Recursion
Journal
Volume
Issue
ISSN
38
4
0097-5397
Citations 
PageRank 
References 
1
0.38
6
Authors
2
Name
Order
Citations
PageRank
Sanjay Jain11647177.87
Frank Stephan231328.88