The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
Finite mixture modelling of class-conditional distributions is a standard method in a statistical pattern recognition. This paper, using bag-of-words vector document representation, explores the use of the mixture of multinomial distributions as a model for class-conditional distribution for multiclass text document classification task. Experimental comparison of the proposed model and the standard Bernoulli and multinomial models as well as the model based on mixture of multivariate Bernoulli distributions...
This paper deals with the likelihood ratio test (LRT) for testing hypotheses
on the mixing
measure in mixture models with or without
structural parameter. The main result gives the asymptotic distribution of the LRT
statistics
under some conditions that are proved to be almost necessary.
A detailed solution is given for two testing problems: the
test of a single distribution against any mixture, with application to Gaussian, Poisson and
binomial distributions; the test of the number of populations...
We study the LRT statistic for testing
a single population i.i.d. model against a mixture of two populations with Markov regime.
We prove that
the LRT statistic converges to infinity in probability
as the number of observations tends to infinity.
This is a consequence of a convergence result
of the LRT statistic for a subproblem where the parameters
are restricted to a subset of the whole parameter set.
The last few years have witnessed important new developments in the theory and practice of pattern classification. We intend to survey some of the main new ideas that have led to these recent results.
The last few years have witnessed important new developments in
the theory and practice of pattern classification. We intend to
survey some of the main new ideas that have led to these
recent results.
Currently displaying 1 –
8 of
8