Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning from Sparse Data by Exploiting Monotonicity Constraints   When training data is sparse, more domain knowledge must be incorporated into
the learning algorithm in order to reduce the effective size of the hypothesis
space. This paper builds on previous work in which knowledge about qualitative
monotonicities was formally represented and incorporated into learning
algorithms (e.g., Clark & Matwin's work with the CN2 rule learning algorithm).
We show how to interpret knowledge of qualitative influences, and in particular
of monotonicities, as constraints on probability distributions, and to
incorporate this knowledge into Bayesian network learning algorithms. We show
that this yields improved accuracy, particularly with very small training sets
(e.g. less than 10 examples).

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The categories of this paper are: comp-gas
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The categories of this paper are: eess
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
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The categories of this paper are: nucl-ex
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The categories of this paper are: bayes-an
2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int
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The categories of this paper are: chem-ph