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Description
  • Questions: Are artificial neural networks useful for the automatic assignment of species composition records from vegetation plots to a priori established classes (vegetation units)? Is the assignment more accurate (1) if the classes are defined by numerical classification rather than by expert-based classification; (2) if the training data set is selected to include plots that are richer in diagnostic species of particular classes? Material: Species composition records (relevés) from 4186 plots of Czech grasslands. Methods: Plots were classified into 11 phytosociological alliances (expert classification) and into 11 clusters derived from numerical cluster analysis. Some plots were used for training the classifiers, which were the multi-layer perceptrons (MLP; a type of artificial neural network). Other plots were used for testing the performance of these classifiers. Plots used for training were selected (1) randomly; (2) according to higher representation of diagnostic species of particular classes.
  • Questions: Are artificial neural networks useful for the automatic assignment of species composition records from vegetation plots to a priori established classes (vegetation units)? Is the assignment more accurate (1) if the classes are defined by numerical classification rather than by expert-based classification; (2) if the training data set is selected to include plots that are richer in diagnostic species of particular classes? Material: Species composition records (relevés) from 4186 plots of Czech grasslands. Methods: Plots were classified into 11 phytosociological alliances (expert classification) and into 11 clusters derived from numerical cluster analysis. Some plots were used for training the classifiers, which were the multi-layer perceptrons (MLP; a type of artificial neural network). Other plots were used for testing the performance of these classifiers. Plots used for training were selected (1) randomly; (2) according to higher representation of diagnostic species of particular classes. (en)
  • Testování umělých neuronových sítí jako metody řízená klasifikace rostlinných společenstev (cs)
Title
  • Supervised classification of plant communities with artificial neural networks
  • Řízená klasifikace rostlinných společenstev pomocí umělých neuronových sítí (cs)
  • Supervised classification of plant communities with artificial neural networks (en)
skos:prefLabel
  • Supervised classification of plant communities with artificial neural networks
  • Řízená klasifikace rostlinných společenstev pomocí umělých neuronových sítí (cs)
  • Supervised classification of plant communities with artificial neural networks (en)
skos:notation
  • RIV/00216224:14310/05:00012592!RIV06-MSM-14310___
http://linked.open.../vavai/riv/strany
  • 407-414
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA206/02/0957), Z(MSM0021622416)
http://linked.open...iv/cisloPeriodika
  • 4
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 545438
http://linked.open...ai/riv/idVysledku
  • RIV/00216224:14310/05:00012592
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Cluster analysis; Grassland; Multi-layer perceptron; Phytosociological data; Predictive habitat modelling; Vegetation survey (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • CZ - Česká republika
http://linked.open...ontrolniKodProRIV
  • [45F660388005]
http://linked.open...i/riv/nazevZdroje
  • Journal of Vegetation Science
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 16
http://linked.open...iv/tvurceVysledku
  • Chytrý, Milan
  • Černá, Lenka
http://linked.open...n/vavai/riv/zamer
issn
  • 1100-9233
number of pages
http://localhost/t...ganizacniJednotka
  • 14310
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