Statistisk signalklassificering utan lärare
(1972) In MSc ThesesDepartment 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:
http://lup.lub.lu.se/student-papers/record/8850587
- author
- Lindquist, Hans and Ericsson, Staffan
- supervisor
- organization
- year
- 1972
- 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}}, }