Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Model of Inductive Bias Learning   A major problem in machine learning is that of inductive bias: how to choose
a learner's hypothesis space so that it is large enough to contain a solution
to the problem being learnt, yet small enough to ensure reliable generalization
from reasonably-sized training sets. Typically such bias is supplied by hand
through the skill and insights of experts. In this paper a model for
automatically learning bias is investigated. The central assumption of the
model is that the learner is embedded within an environment of related learning
tasks. Within such an environment the learner can sample from multiple tasks,
and hence it can search for a hypothesis space that contains good solutions to
many of the problems in the environment. Under certain restrictions on the set
of all hypothesis spaces available to the learner, we show that a hypothesis
space that performs well on a sufficiently large number of training tasks will
also perform well when learning novel tasks in the same environment. Explicit
bounds are also derived demonstrating that learning multiple tasks within an
environment of related tasks can potentially give much better generalization
than learning a single task.

18
The categories of this paper are: econ
21
The categories of this paper are: adap-org
2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
19
The categories of this paper are: eess