Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Search-based Structured Prediction   We describe an adaptation and application of a search-based structured
prediction algorithm "Searn" to unsupervised learning problems. We show that it
is possible to reduce unsupervised learning to supervised learning and
demonstrate a high-quality unsupervised shift-reduce parsing model. We
additionally show a close connection between unsupervised Searn and expectation
maximization. Finally, we demonstrate the efficacy of a semi-supervised
extension. The key idea that enables this is an application of the predict-self
idea for unsupervised learning.

2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas