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Statistisk signalklassificering utan lärare

Lindquist, Hans and Ericsson, Staffan (1972) In MSc Theses
Department of Automatic Control
Abstract
This report deals with the problem of learning to classify observations from a mixture of random variables. The distributions of the random variables are of known functional forms but with unknown parameters. <br><br> This problem is equivalent to that of determining separating hypersurfaces in the range of the random variables. Any rule of classification will be a function of random variables and therefore itself a random variable. Using a statistical approach it is possible to construct a classification rule that will converge to an optimal one with probability one. <br><br> For computational simplicity only the simplest possible case is trated in detail, namely that of mixtures of two one-dimensional distributions. The algorithm is... (More)
This report deals with the problem of learning to classify observations from a mixture of random variables. The distributions of the random variables are of known functional forms but with unknown parameters. <br><br> This problem is equivalent to that of determining separating hypersurfaces in the range of the random variables. Any rule of classification will be a function of random variables and therefore itself a random variable. Using a statistical approach it is possible to construct a classification rule that will converge to an optimal one with probability one. <br><br> For computational simplicity only the simplest possible case is trated in detail, namely that of mixtures of two one-dimensional distributions. The algorithm is tested by simulations on different mixtures of two-parametric distributions. <br><br> An algorithm is constructed that computes the theoretical lower bound of the variance of the classification rule. Numerical results are obtained for a number of cases. (Less)
Please use this url to cite or link to this publication:
author
Lindquist, Hans and Ericsson, Staffan
supervisor
organization
year
type
H3 - Professional qualifications (4 Years - )
subject
publication/series
MSc Theses
report number
TFRT-5107
ISSN
0346-5500
language
Swedish
id
8850587
date added to LUP
2016-03-29 16:02:07
date last changed
2016-03-29 16:02:07
@misc{8850587,
  abstract     = {This report deals with the problem of learning to classify observations from a mixture of random variables. The distributions of the random variables are of known functional forms but with unknown parameters. <br><br> This problem is equivalent to that of determining separating hypersurfaces in the range of the random variables. Any rule of classification will be a function of random variables and therefore itself a random variable. Using a statistical approach it is possible to construct a classification rule that will converge to an optimal one with probability one. <br><br> For computational simplicity only the simplest possible case is trated in detail, namely that of mixtures of two one-dimensional distributions. The algorithm is tested by simulations on different mixtures of two-parametric distributions. <br><br> An algorithm is constructed that computes the theoretical lower bound of the variance of the classification rule. Numerical results are obtained for a number of cases.},
  author       = {Lindquist, Hans and Ericsson, Staffan},
  issn         = {0346-5500},
  language     = {swe},
  note         = {Student Paper},
  series       = {MSc Theses},
  title        = {Statistisk signalklassificering utan lärare},
  year         = {1972},
}