Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: PAC Learning Mixtures of Axis-Aligned Gaussians with No Separation
  Assumption   We propose and analyze a new vantage point for the learning of mixtures of
Gaussians: namely, the PAC-style model of learning probability distributions
introduced by Kearns et al. Here the task is to construct a hypothesis mixture
of Gaussians that is statistically indistinguishable from the actual mixture
generating the data; specifically, the KL-divergence should be at most epsilon.
  In this scenario, we give a poly(n/epsilon)-time algorithm that learns the
class of mixtures of any constant number of axis-aligned Gaussians in
n-dimensional Euclidean space. Our algorithm makes no assumptions about the
separation between the means of the Gaussians, nor does it have any dependence
on the minimum mixing weight. This is in contrast to learning results known in
the ``clustering'' model, where such assumptions are unavoidable.
  Our algorithm relies on the method of moments, and a subalgorithm developed
in previous work by the authors (FOCS 2005) for a discrete mixture-learning
problem.

26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
19
The categories of this paper are: eess
10
The categories of this paper are: q-bio
16
The categories of this paper are: stat
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom