Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On learning parametric-output HMMs   We present a novel approach for learning an HMM whose outputs are distributed
according to a parametric family. This is done by {\em decoupling} the learning
task into two steps: first estimating the output parameters, and then
estimating the hidden states transition probabilities. The first step is
accomplished by fitting a mixture model to the output stationary distribution.
Given the parameters of this mixture model, the second step is formulated as
the solution of an easily solvable convex quadratic program. We provide an
error analysis for the estimated transition probabilities and show they are
robust to small perturbations in the estimates of the mixture parameters.
Finally, we support our analysis with some encouraging empirical results.

35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex