Evolutionary approach to machine learning and deep neural networks: neuro-evolution and gene regulatory networks
This book provides theoretical and practical knowledge about a methodology for evolutionary algorithm-based search strategy with the integration of several machine learning and deep learning techniques. These include convolutional neural networks, Gröbner bases, relevance vector machines, transfer l...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Singapore
Springer
[2018]
|
| Schlagworte: | |
| Online-Zugang: | BTU01 FHA01 FHI01 FHM01 FHN01 FHR01 FKE01 FLA01 FRO01 FWS01 FWS02 HTW01 UBG01 UBM01 UBR01 UBT01 UBW01 UBY01 UER01 UPA01 Volltext |
| Zusammenfassung: | This book provides theoretical and practical knowledge about a methodology for evolutionary algorithm-based search strategy with the integration of several machine learning and deep learning techniques. These include convolutional neural networks, Gröbner bases, relevance vector machines, transfer learning, bagging and boosting methods, clustering techniques (affinity propagation), and belief networks, among others. The development of such tools contributes to better optimizing methodologies. Beginning with the essentials of evolutionary algorithms and covering interdisciplinary research topics, the contents of this book are valuable for different classes of readers: novice, intermediate, and also expert readers from related fields.Following the chapters on introduction and basic methods, Chapter 3 details a new research direction, i.e., neuro-evolution, an evolutionary method for the generation of deep neural networks, and also describes how evolutionary methods are extended in combination with machine learning techniques. Chapter 4 includes novel methods such as particle swarm optimization based on affinity propagation (PSOAP), and transfer learning for differential evolution (TRADE), another machine learning approach for extending differential evolution.The last chapter is dedicated to the state of the art in gene regulatory network (GRN) research as one of the most interesting and active research fields. The author describes an evolving reaction network, which expands the neuro-evolution methodology to produce a type of genetic network suitable for biochemical systems and has succeeded in designing genetic circuits in synthetic biology. The author also presents real-world GRN application to several artificial intelligent tasks, proposing a framework of motion generation by GRNs (MONGERN), which evolves GRNs to operate a real humanoid robot. |
| Beschreibung: | 1 Online-Ressource (XIII, 245 Seiten, 127 illus., 84 illus. in color) |
| ISBN: | 9789811302008 |
| DOI: | 10.1007/978-981-13-0200-8 |
Internformat
MARC
| LEADER | 00000nmm a2200000zc 4500 | ||
|---|---|---|---|
| 001 | BV045099369 | ||
| 003 | DE-604 | ||
| 005 | 20181214 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 180724s2018 |||| o||u| ||||||eng d | ||
| 020 | |a 9789811302008 |c Online |9 978-981-130-200-8 | ||
| 024 | 7 | |a 10.1007/978-981-13-0200-8 |2 doi | |
| 035 | |a (ZDB-2-SCS)9789811302008 | ||
| 035 | |a (OCoLC)1047447747 | ||
| 035 | |a (DE-599)BVBBV045099369 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-355 |a DE-860 |a DE-19 |a DE-Aug4 |a DE-898 |a DE-861 |a DE-523 |a DE-859 |a DE-473 |a DE-29 |a DE-863 |a DE-20 |a DE-862 |a DE-92 |a DE-573 |a DE-M347 |a DE-703 |a DE-706 |a DE-739 |a DE-634 | ||
| 082 | 0 | |a 006.3 |2 23 | |
| 084 | |a ST 300 |0 (DE-625)143650: |2 rvk | ||
| 084 | |a DAT 708f |2 stub | ||
| 084 | |a DAT 718f |2 stub | ||
| 084 | |a DAT 717f |2 stub | ||
| 100 | 1 | |a Iba, Hitoshi |d 1962- |e Verfasser |0 (DE-588)141858443 |4 aut | |
| 245 | 1 | 0 | |a Evolutionary approach to machine learning and deep neural networks |b neuro-evolution and gene regulatory networks |c Hitoshi Iba |
| 264 | 1 | |a Singapore |b Springer |c [2018] | |
| 300 | |a 1 Online-Ressource (XIII, 245 Seiten, 127 illus., 84 illus. in color) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 520 | 3 | |a This book provides theoretical and practical knowledge about a methodology for evolutionary algorithm-based search strategy with the integration of several machine learning and deep learning techniques. These include convolutional neural networks, Gröbner bases, relevance vector machines, transfer learning, bagging and boosting methods, clustering techniques (affinity propagation), and belief networks, among others. The development of such tools contributes to better optimizing methodologies. Beginning with the essentials of evolutionary algorithms and covering interdisciplinary research topics, the contents of this book are valuable for different classes of readers: novice, intermediate, and also expert readers from related fields.Following the chapters on introduction and basic methods, Chapter 3 details a new research direction, i.e., neuro-evolution, an evolutionary method for the generation of deep neural networks, and also describes how evolutionary methods are extended in combination with machine learning techniques. Chapter 4 includes novel methods such as particle swarm optimization based on affinity propagation (PSOAP), and transfer learning for differential evolution (TRADE), another machine learning approach for extending differential evolution.The last chapter is dedicated to the state of the art in gene regulatory network (GRN) research as one of the most interesting and active research fields. The author describes an evolving reaction network, which expands the neuro-evolution methodology to produce a type of genetic network suitable for biochemical systems and has succeeded in designing genetic circuits in synthetic biology. The author also presents real-world GRN application to several artificial intelligent tasks, proposing a framework of motion generation by GRNs (MONGERN), which evolves GRNs to operate a real humanoid robot. | |
| 650 | 4 | |a Computer Science | |
| 650 | 4 | |a Artificial Intelligence (incl. Robotics) | |
| 650 | 4 | |a Bioinformatics | |
| 650 | 4 | |a Mathematical and Computational Biology | |
| 650 | 4 | |a Computational Intelligence | |
| 650 | 4 | |a Computer science | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Bioinformatics | |
| 650 | 4 | |a Biomathematics | |
| 650 | 4 | |a Computational intelligence | |
| 650 | 0 | 7 | |a Deep learning |0 (DE-588)1135597375 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Evolutionärer Algorithmus |0 (DE-588)4366912-8 |2 gnd |9 rswk-swf |
| 653 | |a Künstliche Intelligenz | ||
| 653 | |a Artificial Intelligence (incl. Robotics | ||
| 653 | |a Computer Science | ||
| 653 | |a Bioinformatics | ||
| 653 | |a Bioinformatik | ||
| 653 | |a Biomathematik | ||
| 653 | |a Mathematical and Computational Biology | ||
| 653 | |a Computational Intelligence | ||
| 653 | 0 | |a Artificial intelligence | |
| 653 | 0 | |a Bioinformatics | |
| 653 | 0 | |a Engineering | |
| 653 | 0 | |a Computer science | |
| 653 | 0 | |a Evolutionary Computation;Evolutionary Computation;Meta-Heuristics;Deep Learning;Machine Learning;Gene Regulatory Networks;Particle Swarm Optimization;Differential Evolution;Genetic Programming;Genetic Algorithms | |
| 689 | 0 | 0 | |a Evolutionärer Algorithmus |0 (DE-588)4366912-8 |D s |
| 689 | 0 | 1 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |D s |
| 689 | 0 | 2 | |a Deep learning |0 (DE-588)1135597375 |D s |
| 689 | 0 | |5 DE-604 | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |z 978-981-130-199-5 |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-981-13-0200-8 |x Verlag |z URL des Erstveröffentlichers |3 Volltext |
| 912 | |a ZDB-2-SCS | ||
| 940 | 1 | |q ZDB-2-SCS_2018 | |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-030489973 | ||
| 966 | e | |u https://doi.org/10.1007/978-981-13-0200-8 |l BTU01 |p ZDB-2-SCS |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-981-13-0200-8 |l FHA01 |p ZDB-2-SCS |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-981-13-0200-8 |l FHI01 |p ZDB-2-SCS |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-981-13-0200-8 |l FHM01 |p ZDB-2-SCS |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-981-13-0200-8 |l FHN01 |p ZDB-2-SCS |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-981-13-0200-8 |l FHR01 |p ZDB-2-SCS |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-981-13-0200-8 |l FKE01 |p ZDB-2-SCS |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-981-13-0200-8 |l FLA01 |p ZDB-2-SCS |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-981-13-0200-8 |l FRO01 |p ZDB-2-SCS |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-981-13-0200-8 |l FWS01 |p ZDB-2-SCS |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-981-13-0200-8 |l FWS02 |p ZDB-2-SCS |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-981-13-0200-8 |l HTW01 |p ZDB-2-SCS |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-981-13-0200-8 |l UBG01 |p ZDB-2-SCS |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-981-13-0200-8 |l UBM01 |p ZDB-2-SCS |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-981-13-0200-8 |l UBR01 |p ZDB-2-SCS |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-981-13-0200-8 |l UBT01 |p ZDB-2-SCS |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-981-13-0200-8 |l UBW01 |p ZDB-2-SCS |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-981-13-0200-8 |l UBY01 |p ZDB-2-SCS |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-981-13-0200-8 |l UER01 |p ZDB-2-SCS |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-981-13-0200-8 |l UPA01 |p ZDB-2-SCS |x Verlag |3 Volltext | |
Datensatz im Suchindex
| DE-BY-FWS_katkey | 696569 |
|---|---|
| _version_ | 1806184401730535424 |
| any_adam_object | |
| author | Iba, Hitoshi 1962- |
| author_GND | (DE-588)141858443 |
| author_facet | Iba, Hitoshi 1962- |
| author_role | aut |
| author_sort | Iba, Hitoshi 1962- |
| author_variant | h i hi |
| building | Verbundindex |
| bvnumber | BV045099369 |
| classification_rvk | ST 300 |
| classification_tum | DAT 708f DAT 718f DAT 717f |
| collection | ZDB-2-SCS |
| ctrlnum | (ZDB-2-SCS)9789811302008 (OCoLC)1047447747 (DE-599)BVBBV045099369 |
| dewey-full | 006.3 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3 |
| dewey-search | 006.3 |
| dewey-sort | 16.3 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| doi_str_mv | 10.1007/978-981-13-0200-8 |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>06766nmm a2200997zc 4500</leader><controlfield tag="001">BV045099369</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20181214 </controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">180724s2018 |||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9789811302008</subfield><subfield code="c">Online</subfield><subfield code="9">978-981-130-200-8</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/978-981-13-0200-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-2-SCS)9789811302008</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1047447747</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV045099369</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-355</subfield><subfield code="a">DE-860</subfield><subfield code="a">DE-19</subfield><subfield code="a">DE-Aug4</subfield><subfield code="a">DE-898</subfield><subfield code="a">DE-861</subfield><subfield code="a">DE-523</subfield><subfield code="a">DE-859</subfield><subfield code="a">DE-473</subfield><subfield code="a">DE-29</subfield><subfield code="a">DE-863</subfield><subfield code="a">DE-20</subfield><subfield code="a">DE-862</subfield><subfield code="a">DE-92</subfield><subfield code="a">DE-573</subfield><subfield code="a">DE-M347</subfield><subfield code="a">DE-703</subfield><subfield code="a">DE-706</subfield><subfield code="a">DE-739</subfield><subfield code="a">DE-634</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">006.3</subfield><subfield code="2">23</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 300</subfield><subfield code="0">(DE-625)143650:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">DAT 708f</subfield><subfield code="2">stub</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">DAT 718f</subfield><subfield code="2">stub</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">DAT 717f</subfield><subfield code="2">stub</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Iba, Hitoshi</subfield><subfield code="d">1962-</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)141858443</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Evolutionary approach to machine learning and deep neural networks</subfield><subfield code="b">neuro-evolution and gene regulatory networks</subfield><subfield code="c">Hitoshi Iba</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Singapore</subfield><subfield code="b">Springer</subfield><subfield code="c">[2018]</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (XIII, 245 Seiten, 127 illus., 84 illus. in color)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1="3" ind2=" "><subfield code="a">This book provides theoretical and practical knowledge about a methodology for evolutionary algorithm-based search strategy with the integration of several machine learning and deep learning techniques. These include convolutional neural networks, Gröbner bases, relevance vector machines, transfer learning, bagging and boosting methods, clustering techniques (affinity propagation), and belief networks, among others. The development of such tools contributes to better optimizing methodologies. Beginning with the essentials of evolutionary algorithms and covering interdisciplinary research topics, the contents of this book are valuable for different classes of readers: novice, intermediate, and also expert readers from related fields.Following the chapters on introduction and basic methods, Chapter 3 details a new research direction, i.e., neuro-evolution, an evolutionary method for the generation of deep neural networks, and also describes how evolutionary methods are extended in combination with machine learning techniques. Chapter 4 includes novel methods such as particle swarm optimization based on affinity propagation (PSOAP), and transfer learning for differential evolution (TRADE), another machine learning approach for extending differential evolution.The last chapter is dedicated to the state of the art in gene regulatory network (GRN) research as one of the most interesting and active research fields. The author describes an evolving reaction network, which expands the neuro-evolution methodology to produce a type of genetic network suitable for biochemical systems and has succeeded in designing genetic circuits in synthetic biology. The author also presents real-world GRN application to several artificial intelligent tasks, proposing a framework of motion generation by GRNs (MONGERN), which evolves GRNs to operate a real humanoid robot.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer Science</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Artificial Intelligence (incl. Robotics)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bioinformatics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mathematical and Computational Biology</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computational Intelligence</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer science</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Artificial intelligence</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bioinformatics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Biomathematics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computational intelligence</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Deep learning</subfield><subfield code="0">(DE-588)1135597375</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Evolutionärer Algorithmus</subfield><subfield code="0">(DE-588)4366912-8</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Künstliche Intelligenz</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Artificial Intelligence (incl. Robotics</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Computer Science</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Bioinformatics</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Bioinformatik</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Biomathematik</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Mathematical and Computational Biology</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Computational Intelligence</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Artificial intelligence</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Bioinformatics</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Engineering</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Computer science</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Evolutionary Computation;Evolutionary Computation;Meta-Heuristics;Deep Learning;Machine Learning;Gene Regulatory Networks;Particle Swarm Optimization;Differential Evolution;Genetic Programming;Genetic Algorithms</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Evolutionärer Algorithmus</subfield><subfield code="0">(DE-588)4366912-8</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">Deep learning</subfield><subfield code="0">(DE-588)1135597375</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="z">978-981-130-199-5</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1007/978-981-13-0200-8</subfield><subfield code="x">Verlag</subfield><subfield code="z">URL des Erstveröffentlichers</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-2-SCS</subfield></datafield><datafield tag="940" ind1="1" ind2=" "><subfield code="q">ZDB-2-SCS_2018</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-030489973</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-981-13-0200-8</subfield><subfield code="l">BTU01</subfield><subfield code="p">ZDB-2-SCS</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-981-13-0200-8</subfield><subfield code="l">FHA01</subfield><subfield code="p">ZDB-2-SCS</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-981-13-0200-8</subfield><subfield code="l">FHI01</subfield><subfield code="p">ZDB-2-SCS</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-981-13-0200-8</subfield><subfield code="l">FHM01</subfield><subfield code="p">ZDB-2-SCS</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-981-13-0200-8</subfield><subfield code="l">FHN01</subfield><subfield code="p">ZDB-2-SCS</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-981-13-0200-8</subfield><subfield code="l">FHR01</subfield><subfield code="p">ZDB-2-SCS</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-981-13-0200-8</subfield><subfield code="l">FKE01</subfield><subfield code="p">ZDB-2-SCS</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-981-13-0200-8</subfield><subfield code="l">FLA01</subfield><subfield code="p">ZDB-2-SCS</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-981-13-0200-8</subfield><subfield code="l">FRO01</subfield><subfield code="p">ZDB-2-SCS</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-981-13-0200-8</subfield><subfield code="l">FWS01</subfield><subfield code="p">ZDB-2-SCS</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-981-13-0200-8</subfield><subfield code="l">FWS02</subfield><subfield code="p">ZDB-2-SCS</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-981-13-0200-8</subfield><subfield code="l">HTW01</subfield><subfield code="p">ZDB-2-SCS</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-981-13-0200-8</subfield><subfield code="l">UBG01</subfield><subfield code="p">ZDB-2-SCS</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-981-13-0200-8</subfield><subfield code="l">UBM01</subfield><subfield code="p">ZDB-2-SCS</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-981-13-0200-8</subfield><subfield code="l">UBR01</subfield><subfield code="p">ZDB-2-SCS</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-981-13-0200-8</subfield><subfield code="l">UBT01</subfield><subfield code="p">ZDB-2-SCS</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-981-13-0200-8</subfield><subfield code="l">UBW01</subfield><subfield code="p">ZDB-2-SCS</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-981-13-0200-8</subfield><subfield code="l">UBY01</subfield><subfield code="p">ZDB-2-SCS</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-981-13-0200-8</subfield><subfield code="l">UER01</subfield><subfield code="p">ZDB-2-SCS</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-981-13-0200-8</subfield><subfield code="l">UPA01</subfield><subfield code="p">ZDB-2-SCS</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV045099369 |
| illustrated | Not Illustrated |
| indexdate | 2024-08-01T13:27:54Z |
| institution | BVB |
| isbn | 9789811302008 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030489973 |
| oclc_num | 1047447747 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-860 DE-19 DE-BY-UBM DE-Aug4 DE-898 DE-BY-UBR DE-861 DE-523 DE-859 DE-473 DE-BY-UBG DE-29 DE-863 DE-BY-FWS DE-20 DE-862 DE-BY-FWS DE-92 DE-573 DE-M347 DE-703 DE-706 DE-739 DE-634 |
| owner_facet | DE-355 DE-BY-UBR DE-860 DE-19 DE-BY-UBM DE-Aug4 DE-898 DE-BY-UBR DE-861 DE-523 DE-859 DE-473 DE-BY-UBG DE-29 DE-863 DE-BY-FWS DE-20 DE-862 DE-BY-FWS DE-92 DE-573 DE-M347 DE-703 DE-706 DE-739 DE-634 |
| physical | 1 Online-Ressource (XIII, 245 Seiten, 127 illus., 84 illus. in color) |
| psigel | ZDB-2-SCS ZDB-2-SCS_2018 |
| publishDate | 2018 |
| publishDateSearch | 2018 |
| publishDateSort | 2018 |
| publisher | Springer |
| record_format | marc |
| spellingShingle | Iba, Hitoshi 1962- Evolutionary approach to machine learning and deep neural networks neuro-evolution and gene regulatory networks Computer Science Artificial Intelligence (incl. Robotics) Bioinformatics Mathematical and Computational Biology Computational Intelligence Computer science Artificial intelligence Biomathematics Computational intelligence Deep learning (DE-588)1135597375 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Evolutionärer Algorithmus (DE-588)4366912-8 gnd |
| subject_GND | (DE-588)1135597375 (DE-588)4193754-5 (DE-588)4366912-8 |
| title | Evolutionary approach to machine learning and deep neural networks neuro-evolution and gene regulatory networks |
| title_auth | Evolutionary approach to machine learning and deep neural networks neuro-evolution and gene regulatory networks |
| title_exact_search | Evolutionary approach to machine learning and deep neural networks neuro-evolution and gene regulatory networks |
| title_full | Evolutionary approach to machine learning and deep neural networks neuro-evolution and gene regulatory networks Hitoshi Iba |
| title_fullStr | Evolutionary approach to machine learning and deep neural networks neuro-evolution and gene regulatory networks Hitoshi Iba |
| title_full_unstemmed | Evolutionary approach to machine learning and deep neural networks neuro-evolution and gene regulatory networks Hitoshi Iba |
| title_short | Evolutionary approach to machine learning and deep neural networks |
| title_sort | evolutionary approach to machine learning and deep neural networks neuro evolution and gene regulatory networks |
| title_sub | neuro-evolution and gene regulatory networks |
| topic | Computer Science Artificial Intelligence (incl. Robotics) Bioinformatics Mathematical and Computational Biology Computational Intelligence Computer science Artificial intelligence Biomathematics Computational intelligence Deep learning (DE-588)1135597375 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Evolutionärer Algorithmus (DE-588)4366912-8 gnd |
| topic_facet | Computer Science Artificial Intelligence (incl. Robotics) Bioinformatics Mathematical and Computational Biology Computational Intelligence Computer science Artificial intelligence Biomathematics Computational intelligence Deep learning Maschinelles Lernen Evolutionärer Algorithmus |
| url | https://doi.org/10.1007/978-981-13-0200-8 |
| work_keys_str_mv | AT ibahitoshi evolutionaryapproachtomachinelearninganddeepneuralnetworksneuroevolutionandgeneregulatorynetworks |