274 research outputs found
Handwritten Character Recognition of South Indian Scripts: A Review
Handwritten character recognition is always a frontier area of research in
the field of pattern recognition and image processing and there is a large
demand for OCR on hand written documents. Even though, sufficient studies have
performed in foreign scripts like Chinese, Japanese and Arabic characters, only
a very few work can be traced for handwritten character recognition of Indian
scripts especially for the South Indian scripts. This paper provides an
overview of offline handwritten character recognition in South Indian Scripts,
namely Malayalam, Tamil, Kannada and Telungu.Comment: Paper presented on the "National Conference on Indian Language
Computing", Kochi, February 19-20, 2011. 6 pages, 5 figure
Model Selection and Adaptive Markov chain Monte Carlo for Bayesian Cointegrated VAR model
This paper develops a matrix-variate adaptive Markov chain Monte Carlo (MCMC)
methodology for Bayesian Cointegrated Vector Auto Regressions (CVAR). We
replace the popular approach to sampling Bayesian CVAR models, involving griddy
Gibbs, with an automated efficient alternative, based on the Adaptive
Metropolis algorithm of Roberts and Rosenthal, (2009). Developing the adaptive
MCMC framework for Bayesian CVAR models allows for efficient estimation of
posterior parameters in significantly higher dimensional CVAR series than
previously possible with existing griddy Gibbs samplers. For a n-dimensional
CVAR series, the matrix-variate posterior is in dimension , with
significant correlation present between the blocks of matrix random variables.
We also treat the rank of the CVAR model as a random variable and perform joint
inference on the rank and model parameters. This is achieved with a Bayesian
posterior distribution defined over both the rank and the CVAR model
parameters, and inference is made via Bayes Factor analysis of rank.
Practically the adaptive sampler also aids in the development of automated
Bayesian cointegration models for algorithmic trading systems considering
instruments made up of several assets, such as currency baskets. Previously the
literature on financial applications of CVAR trading models typically only
considers pairs trading (n=2) due to the computational cost of the griddy
Gibbs. We are able to extend under our adaptive framework to and
demonstrate an example with n = 10, resulting in a posterior distribution with
parameters up to dimension 310. By also considering the rank as a random
quantity we can ensure our resulting trading models are able to adjust to
potentially time varying market conditions in a coherent statistical framework.Comment: to appear journal Bayesian Analysi
Morphological filtering on hypergraphs
The focus of this article is to develop computationally efficient
mathematical morphology operators on hypergraphs. To this aim we consider
lattice structures on hypergraphs on which we build morphological operators. We
develop a pair of dual adjunctions between the vertex set and the hyper edge
set of a hypergraph H, by defining a vertex-hyperedge correspondence. This
allows us to recover the classical notion of a dilation/erosion of a subset of
vertices and to extend it to subhypergraphs of H. Afterward, we propose several
new openings, closings, granulometries and alternate sequential filters acting
(i) on the subsets of the vertex and hyperedge set of H and (ii) on the
subhypergraphs of a hypergraph
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