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010   $a3-319-75049-6
024 7 $a10.1007/978-3-319-75049-1
035   $a(CKB)4100000002485481
035   $a(MiAaPQ)EBC5347190
035   $a(DE-He213)978-3-319-75049-1
035   $a(PPN)224639323
035   $a(EXLCZ)994100000002485481
100   $a20180224d2018      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aArtificial Adaptive Systems Using Auto Contractive Maps $eTheory, Applications and Extensions /$fby Paolo Massimo Buscema, Giulia Massini, Marco Breda, Weldon A. Lodwick, Francis Newman, Masoud Asadi-Zeydabadi
205   $a1st ed. 2018.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018.
215   $a1 online resource (184 pages)
225 1 $aStudies in Systems, Decision and Control,$x2198-4182 ;$v131
311   $a3-319-75048-8 
327   $aAn Introduction -- Artificial Neural Networks -- Auto-Contractive Maps -- Visualization of Auto-CM Output -- Dataset Transformations and Auto-CM -- Comparison of Auto-CM to Various Other Data Understanding Approaches.
330   $aThis book offers an introduction to artificial adaptive systems and a general model of the relationships between the data and algorithms used to analyze them. It subsequently describes artificial neural networks as a subclass of artificial adaptive systems, and reports on the backpropagation algorithm, while also identifying an important connection between supervised and unsupervised artificial neural networks. The book?s primary focus is on the auto contractive map, an unsupervised artificial neural network employing a fixed point method versus traditional energy minimization. This is a powerful tool for understanding, associating and transforming data, as demonstrated in the numerous examples presented here. A supervised version of the auto contracting map is also introduced as an outstanding method for recognizing digits and defects. In closing, the book walks the readers through the theory and examples of how the auto contracting map can be used in conjunction with another artificial neural network, the ?spin-net,? as a dynamic form of auto-associative memory.
410  0$aStudies in Systems, Decision and Control,$x2198-4182 ;$v131
606   $aComputational intelligence
606   $aData mining
606   $aArtificial intelligence
606   $aMathematical logic
606   $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014
606   $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
606   $aMathematical Logic and Foundations$3https://scigraph.springernature.com/ontologies/product-market-codes/M24005
615  0$aComputational intelligence.
615  0$aData mining.
615  0$aArtificial intelligence.
615  0$aMathematical logic.
615 14$aComputational Intelligence.
615 24$aData Mining and Knowledge Discovery.
615 24$aArtificial Intelligence.
615 24$aMathematical Logic and Foundations.
676   $a004
700   $aBuscema$b Paolo Massimo$4aut$4http://id.loc.gov/vocabulary/relators/aut$0721441
702   $aMassini$b Giulia$4aut$4http://id.loc.gov/vocabulary/relators/aut
702   $aBreda$b Marco$4aut$4http://id.loc.gov/vocabulary/relators/aut
702   $aLodwick$b Weldon A$4aut$4http://id.loc.gov/vocabulary/relators/aut
702   $aNewman$b Francis$4aut$4http://id.loc.gov/vocabulary/relators/aut
702   $aAsadi-Zeydabadi$b Masoud$4aut$4http://id.loc.gov/vocabulary/relators/aut
906   $aBOOK
912   $a9910299947603321
996   $aArtificial Adaptive Systems Using Auto Contractive Maps$92521979
997   $aUNINA