Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bounds in Query Learning   We introduce new combinatorial quantities for concept classes, and prove
lower and upper bounds for learning complexity in several models of query
learning in terms of various combinatorial quantities. Our approach is flexible
and powerful enough to enough to give new and very short proofs of the
efficient learnability of several prominent examples (e.g. regular languages
and regular $\omega$-languages), in some cases also producing new bounds on the
number of queries. In the setting of equivalence plus membership queries, we
give an algorithm which learns a class in polynomially many queries whenever
any such algorithm exists.
  We also study equivalence query learning in a randomized model, producing new
bounds on the expected number of queries required to learn an arbitrary
concept. Many of the techniques and notions of dimension draw inspiration from
or are related to notions from model theory, and these connections are
explained. We also use techniques from query learning to mildly improve a
result of Laskowski regarding compression schemes.

33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph
17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex