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020 _a9783319750491
_9978-3-319-75049-1
024 7 _a10.1007/978-3-319-75049-1
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aTEC009000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aBuscema, Paolo Massimo.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_956507
245 1 0 _aArtificial Adaptive Systems Using Auto Contractive Maps
_h[electronic resource] :
_bTheory, Applications and Extensions /
_cby Paolo Massimo Buscema, Giulia Massini, Marco Breda, Weldon A. Lodwick, Francis Newman, Masoud Asadi-Zeydabadi.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aVII, 179 p. 97 illus., 74 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Systems, Decision and Control,
_x2198-4190 ;
_v131
505 0 _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.
520 _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.
650 0 _aComputational intelligence.
_97716
650 0 _aData mining.
_93907
650 0 _aArtificial intelligence.
_93407
650 0 _aMathematical logic.
_92258
650 1 4 _aComputational Intelligence.
_97716
650 2 4 _aData Mining and Knowledge Discovery.
_956508
650 2 4 _aArtificial Intelligence.
_93407
650 2 4 _aMathematical Logic and Foundations.
_934712
700 1 _aMassini, Giulia.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_956509
700 1 _aBreda, Marco.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_956510
700 1 _aLodwick, Weldon A.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_956511
700 1 _aNewman, Francis.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_956512
700 1 _aAsadi-Zeydabadi, Masoud.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_956513
710 2 _aSpringerLink (Online service)
_956514
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319750484
776 0 8 _iPrinted edition:
_z9783319750507
776 0 8 _iPrinted edition:
_z9783030091354
830 0 _aStudies in Systems, Decision and Control,
_x2198-4190 ;
_v131
_956515
856 4 0 _uhttps://doi.org/10.1007/978-3-319-75049-1
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c79757
_d79757