000 | 03342nam a22005415i 4500 | ||
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001 | 978-3-658-38955-0 | ||
003 | DE-He213 | ||
005 | 20240423130147.0 | ||
007 | cr nn 008mamaa | ||
008 | 220806s2022 gw | s |||| 0|eng d | ||
020 |
_a9783658389550 _9978-3-658-38955-0 |
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024 | 7 |
_a10.1007/978-3-658-38955-0 _2doi |
|
050 | 4 | _aQ334-342 | |
050 | 4 | _aTA347.A78 | |
072 | 7 |
_aUYQ _2bicssc |
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072 | 7 |
_aCOM004000 _2bisacsh |
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072 | 7 |
_aUYQ _2thema |
|
082 | 0 | 4 |
_a006.3 _223 |
100 | 1 |
_aKnaup, Julian. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
245 | 1 | 0 |
_aImpact of Class Assignment on Multinomial Classification Using Multi-Valued Neurons _h[electronic resource] / _cby Julian Knaup. |
250 | _a1st ed. 2022. | ||
264 | 1 |
_aWiesbaden : _bSpringer Fachmedien Wiesbaden : _bImprint: Springer Vieweg, _c2022. |
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300 |
_aXII, 77 p. 44 illus. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aBestMasters, _x2625-3615 |
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505 | 0 | _a1 Introduction -- 2 Preliminaries -- 3 Scientific State of the Art -- 4 Approach -- 5 Evaluation -- 6 Conclusion and Outlook. | |
520 | _aMultilayer neural networks based on multi-valued neurons (MLMVNs) have been proposed to combine the advantages of complex-valued neural networks with a plain derivative-free learning algorithm. In addition, multi-valued neurons (MVNs) offer a multi-valued threshold logic resulting in the ability to replace multiple conventional output neurons in classification tasks. Therefore, several classes can be assigned to one output neuron. This book introduces a novel approach to assign multiple classes to numerous MVNs in the output layer. It was found that classes that possess similarities should be allocated to the same neuron and arranged adjacent to each other on the unit circle. Since MLMVNs require input data located on the unit circle, two employed transformations are reevaluated. The min-max scaler utilizing the exponential function, and the 2D discrete Fourier transform restricting to the phase information for image recognition. The evaluation was performed on the Sensorless Drive Diagnosis dataset and the Fashion MNIST dataset. About the Author Julian Knaup received his B. Sc. in Electrical Engineering and his M. Sc. in Information Technology from the University of Applied Sciences and Arts Ostwestfalen-Lippe. He is currently working on machine learning algorithms at the Institute Industrial IT and researching AI potentials in product creation. | ||
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aMachine learning. | |
650 | 0 |
_aMathematics _xData processing. |
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650 | 1 | 4 | _aArtificial Intelligence. |
650 | 2 | 4 | _aMachine Learning. |
650 | 2 | 4 | _aComputational Science and Engineering. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783658389543 |
776 | 0 | 8 |
_iPrinted edition: _z9783658389567 |
830 | 0 |
_aBestMasters, _x2625-3615 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-658-38955-0 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
999 |
_c185715 _d185715 |