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On the Learnability of Vector Spaces
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On the Learnability of Vector Spaces
Valentina S. Harizanov4 and Frank Stephan5 
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The Department of Mathematics, The George Washington University, Funger Hall, 2201 G Street, DC, 20052 Washington, USA |
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Mathematisches Institut, Universität Heidelberg, Im Neuenheimer Feld 294, 69120 Heidelberg, Germany, EU |
Abstract
The central topic of the paper is the learnability of the recursively enumerable subspaces of V
∞/V , where V
∞ is the standard recursive vector space over the rationals with countably infinite dimension, and V is a given recursively enumerable subspace of V
∞. It is shown that certain types of vector spaces can be characterized in terms of learnability properties: V
∞/V is behaviourally correct learnable from text iff V is finitely dimensional, V
∞/V is behaviourally correct learnable from switching type of information iff V is finite-dimensional, 0-thin, or 1-thin. On the other hand, learnability from an informant does not correspond to similar
algebraic properties of a given space. There are 0-thin spaces W
1 and W
2 such that W
1 is not explanatorily learnable from informant and the infinite product (W
1)∞ is not behaviourally correct learnable, while W
2 and the infinite product (W
2)∞ are both explanatorily learnable from informant.
Valentina Harizanov was partially supported by the UFF grant of the George Washington University.
Frank Stephan was supported by the Deutsche Forschungsgemeinschaft (DFG) Heisenberg grant Ste 967/1-1.
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