Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Resource-bounded Dimension in Computational Learning Theory   This paper focuses on the relation between computational learning theory and
resource-bounded dimension. We intend to establish close connections between
the learnability/nonlearnability of a concept class and its corresponding size
in terms of effective dimension, which will allow the use of powerful dimension
techniques in computational learning and viceversa, the import of learning
results into complexity via dimension. Firstly, we obtain a tight result on the
dimension of online mistake-bound learnable classes. Secondly, in relation with
PAC learning, we show that the polynomial-space dimension of PAC learnable
classes of concepts is zero. This provides a hypothesis on effective dimension
that implies the inherent unpredictability of concept classes (the classes that
verify this property are classes not efficiently PAC learnable using any
hypothesis). Thirdly, in relation to space dimension of classes that are
learnable by membership query algorithms, the main result proves that
polynomial-space dimension of concept classes learnable by a membership-query
algorithm is zero.

22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org
24
The categories of this paper are: dg-ga
15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
17
The categories of this paper are: q-fin