An information-theoretic approach to neural computing:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York [u.a.]
Springer
1996
|
| Schriftenreihe: | Perspectives in neural computing
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XIII, 261 S. graph. Darst. |
| ISBN: | 0387946667 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV010798021 | ||
| 003 | DE-604 | ||
| 005 | 20150311 | ||
| 007 | t | ||
| 008 | 960617s1996 d||| |||| 00||| eng d | ||
| 020 | |a 0387946667 |9 0-387-94666-7 | ||
| 035 | |a (OCoLC)246816567 | ||
| 035 | |a (DE-599)BVBBV010798021 | ||
| 040 | |a DE-604 |b ger |e rakddb | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-20 |a DE-29T |a DE-91G |a DE-739 |a DE-19 |a DE-355 |a DE-706 |a DE-634 |a DE-83 |a DE-188 | ||
| 050 | 0 | |a QA76.87 | |
| 082 | 0 | |a 006.3 | |
| 084 | |a ST 130 |0 (DE-625)143588: |2 rvk | ||
| 084 | |a ST 150 |0 (DE-625)143594: |2 rvk | ||
| 084 | |a ST 152 |0 (DE-625)143596: |2 rvk | ||
| 084 | |a ST 285 |0 (DE-625)143648: |2 rvk | ||
| 084 | |a ST 300 |0 (DE-625)143650: |2 rvk | ||
| 084 | |a ST 301 |0 (DE-625)143651: |2 rvk | ||
| 084 | |a ELT 505f |2 stub | ||
| 084 | |a DAT 717f |2 stub | ||
| 100 | 1 | |a Deco, Gustavo |d 1961- |e Verfasser |0 (DE-588)123302803 |4 aut | |
| 245 | 1 | 0 | |a An information-theoretic approach to neural computing |c Gustavo Deco ; Dragan Obradovic |
| 246 | 1 | 3 | |a An information theoretic approach to neural computing |
| 264 | 1 | |a New York [u.a.] |b Springer |c 1996 | |
| 300 | |a XIII, 261 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Perspectives in neural computing | |
| 650 | 4 | |a Neural networks (Computer science) | |
| 650 | 0 | 7 | |a Neuronales Netz |0 (DE-588)4226127-2 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Informationstheorie |0 (DE-588)4026927-9 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Neurocomputer |0 (DE-588)4200446-9 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Neuronales Netz |0 (DE-588)4226127-2 |D s |
| 689 | 0 | 1 | |a Informationstheorie |0 (DE-588)4026927-9 |D s |
| 689 | 0 | |5 DE-604 | |
| 689 | 1 | 0 | |a Neurocomputer |0 (DE-588)4200446-9 |D s |
| 689 | 1 | |8 1\p |5 DE-604 | |
| 700 | 1 | |a Obradovic, Dragan |e Verfasser |4 aut | |
| 856 | 4 | 2 | |m GBV Datenaustausch |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=007212561&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-007212561 | ||
| 883 | 1 | |8 1\p |a cgwrk |d 20201028 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | |
Datensatz im Suchindex
| _version_ | 1804125280519847936 |
|---|---|
| adam_text | GUSTAVO DECO DRAGAN OBRADOVIC AN INFORMATION- THEORETIC APPROACH TO
NEURAL COMPUTING WITH 52 ILLUSTRATIONS SPRINGER CONTENTS ACKNOWLEDGMENTS
VI FOREWORD VII CHAPTER 1 INTRODUCTION 1 CHAPTER 2 PRELIMINARIES OF
INFORMATION THEORY AND NEURAL NETWORKS 7 2.1 ELEMENTS OF INFORMATION
THEORY 8 2.1.1 ENTROPY AND INFORMATION 8 2.1.2 JOINT ENTROPY AND
CONDITIONAL ENTROPY 9 2.1.3 KULLBACK-LEIBLER ENTROPY 9 2.1.4 MUTUAL
INFORMATION 10 2.1.5 DIFFERENTIAL ENTROPY, RELATIVE ENTROPY AND MUTUAL
INFORMATION 11 2.1.6 CHAIN RULES 13 2.1.7 FUNDAMENTAL INFORMATION THEORY
INEQUALITIES 15 2.1.8 CODING THEORY 21 4 2.2 ELEMENTS OF THE THEORY OF
NEURAL NETWORKS 23 *2.2.1 NEURAL NETWORK MODELING 23 2.2.2 NEURAL
ARCHITECTURES 24 - 2.2.3 LEARNING PARADIGMS 27 * 2.2.4 FEEDFORWARD
NETWORKS: BACKPROPAGATION 28 2.2.5 STOCHASTIC RECURRENT NETWORKS:
BOLTZMANN MACHINE 31 2.2.6 UNSUPERVISED COMPETITIVE LEARNING 35 2.2.7
BIOLOGICAL LEARNING RULES 36 PART I: UNSUPERVISED LEARNING CHAPTER 3
LINEAR FEATURE EXTRACTION: INFOMAX PRINCIPLE 41 3.1 PRINCIPAL COMPONENT
ANALYSIS: STATISTICAL APPROACH 42 3.1.1 PCA AND DIAGONALIZATION OF THE
COVARIANCE MATRIX 42 3.1.2 PCA AND OPTIMAL RECONSTRUCTION 45 CONTENTS
CHAPTER 4 CHAPTER 5 - 3.1.3 NEURAL NETWORK ALGORITHMS AND PCA 51 3.2
INFORMATION THEORETIC APPROACH: INFOMAX 57 3.2.1 MINIMIZATION OF
INFORMATION LOSS PRINCIPLE AND INFOMAX PRINCIPLE 58 3.2.2 UPPER BOUND OF
INFORMATION LOSS 59 3.2.3 INFORMATION CAPACITY AS A LYAPUNOV FUNCTION OF
THE GENERAL STOCHASTIC APPROXIMATION 61 INDEPENDENT COMPONENT ANALYSIS:
GENERAL FORMULATION AND LINEAR CASE 65 4.1 ICA-DEFMITION 67 4.2 GENERAL
CRITERIA FOR ICA 68 4.2.1 CUMULANT EXPANSION BASED CRITERION FOR ICA 69
4.2.2 MUTUAL INFORMATION AS CRITERION FOR ICA 73 4.3 LINEAR ICA 79 4.4
GAUSSIAN INPUT DISTRIBUTION AND LINEAR ICA 81 4.4.1 NETWORKS WITH
ANTI-SYMMETRIC LATERAL CONNECTIONS 84 4.4.2 NETWORKS WITH SYMMETRIC
LATERAL CONNECTIONS 86 4.4.3 EXAMPLES OF LEARNING WITH SYMMETRIC AND
ANTI-SYMMETRIC NETWORKS 89 4.5 LEARNING IN GAUSSIAN ICA WITH ROTATION
MATRICES: PCA 91 4.5.1 RELATIONSHIP BETWEEN PCA AND ICA IN GAUSSIAN
INPUT CASE 93 4.5.2 LINEAR GAUSSIAN ICA AND THE OUTPUT DIMENSION
REDUCTION 94 4.6 LINEAR ICA IN ARBITRARY INPUT DISTRIBUTION 95 4.6.1
SOME PROPERTIES OF CUMULANTS AT THE OUTPUT OF A LINEAR TRANSFORMATION 95
4.6.2 THE EDGEWORTH EXPANSION CRITERIA AND THEOREM 4.6.2 99 4.6.3
ALGORITHMS FOR OUTPUT FACTORIZATION IN THE NON-GAUSSIAN CASE 100 4.6.4
EXPERIMENTAL RESULTS OF LINEAR ICA ALGORITHMS IN THE NON-GAUSSIAN CASE
102 NONLINEAR FEATURE EXTRACTION: BOOLEAN STOCHASTIC NETWORKS 109 5.1
INFOMAX PRINCIPLE FOR BOLTZMANN MACHINES 110 CONTENTS 5.1.1 LEARNING
MODEL 110 5.1.2 EXAMPLES OF INFOMAX PRINCIPLE IN BOLTZMANN MACHINE 113
5.2 REDUNDANCY MINIMIZATION AND INFOMAX FOR THE BOLTZMANN MACHINE , 119
5.2.1 LEARNING MODEL 119 5.2.2 NUMERICAL COMPLEXITY OF THE LEARNING RULE
124 5.2.3 FACTORIAL LEARNING EXPERIMENTS 124 5.2.4 RECEPTIVE FIELDS
FORMATION FROM A RETINA 129 5.3 APPENDIX 132 CHAPTER 6 NONLINEAR FEATURE
EXTRACTION: DETERMINISTIC NEURAL NETWORKS 135 6.1 REDUNDANCY REDUCTION
BY TRIANGULAR VOLUME CONSERVING ARCHITECTURES 136 6.1.1 NETWORKS WITH
LINEAR, SIGMOIDAL AND HIGHER ORDER ACTIVATION FUNCTIONS 140 6.1.2
SIMULATIONS AND RESULTS 142 6.2 UNSUPERVISED MODELING OF CHAOTIC TIME
SERIES 146 6.2.1 DYNAMICAL SYSTEM MODELING 147 6.3 REDUNDANCY REDUCTION
BY GENERAL SYMPLECTIC ARCHITECTURES 156 6.3.1 GENERAL ENTROPY PRESERVING
NONLINEAR MAPS 156 6.3.2 OPTIMIZING A PARAMETERIZED SYMPLECTIC MAP 157
6.3.3 DENSITY ESTIMATION AND NOVELTY DETECTION 159 6.4 EXAMPLE: THEORY
OF EARLY VISION 163 6.4.1 THEORETICAL BACKGROUND 164 6.4.2 RETINA MODEL
165 PART II: SUPERVISED LEARNING CHAPTER 7 SUPERVISED LEARNING AND
STATISTICAL ESTIMATION 7.1 STATISTICAL PARAMETER ESTIMATION - BASIC
DEFINITIONS 7.1.1 CRAMER-RAO INEQUALITY FOR UNBIASED ESTIMATORS 7.2
MAXIMUM LIKELIHOOD ESTIMATORS 169 171 172 175 XLI CONTENTS CHAPTER 8
CHAPTER 9 CHAPTER 10 7.2.1 MAXIMUM LIKELIHOOD AND THE INFORMATION
MEASURE 176 7.3 MAXIMUM A POSTERIORI ESTIMATION 178 7.4 EXTENSIONS OF
MLE TO INCLUDE MODEL SELECTION 179 7.4.1 AKAIKE S INFORMATION THEORETIC
CRITERION (AIC) 179 7.4.2 MINIMAL DESCRIPTION LENGTH AND STOCHASTIC
COMPLEXITY 183 7.5 GENERALIZATION AND LEARNING ON THE SAME DATA SET 185
STATISTICAL PHYSICS THEORY OF SUPERVISED LEARNING AND GENERALIZATION 187
8.1 STATISTICAL MECHANICS THEORY OF SUPERVISED LEARNING 188 8.1.1
MAXIMUM ENTROPY PRINCIPLE 189 8.1.2 PROBABILITY INFERENCE WITH AN
ENSEMBLE OF NETWORKS 192 8.1.3 INFORMATION GAIN AND COMPLEXITY ANALYSIS
195 8.2 LEARNING WITH HIGHER ORDER NEURAL NETWORKS 198 8.2.1 PARTITION
FUNCTION EVALUATION 198 8.2.2 INFORMATION GAIN IN POLYNOMIAL NETWORKS
202 8.2.3 NUMERICAL EXPERIMENTS 203 8.3 LEARNING WITH GENERAL
FEEDFORWARD NEURAL NETWORKS 205 8.3.1 PARTITION FUNCTION APPROXIMATION
205 8.3.2 NUMERICAL EXPERIMENTS 207 8.4 STATISTICAL THEORY OF
UNSUPERVISED AND SUPERVISED FACTORIAL LEARNING 208 8.4.1 STATISTICAL
THEORY OF UNSUPERVISED FACTORIAL LEARNING 208 8.4.2 DUALITY BETWEEN
UNSUPERVISED AND MAXIMUM LIKELIHOOD BASED SUPERVISED LEARNING 213
COMPOSITE NETWORKS 219 9.1 COOPERATION AND SPECIALIZATION IN COMPOSITE
NETWORKS 220 9.2 COMPOSITE MODELS AS GAUSSIAN MIXTURES 222 INFORMATION
THEORY BASED REGULARIZING METHODS 225 10.1 THEORETICAL FRAMEWORK 226
CONTENTS 10.1.1 NETWORK COMPLEXITY REGULATION 226 10.1.2 NETWORK
ARCHITECTURE AND LEARNING PARADIGM 227 10.1.3 APPLICATIONS OF THE MUTUAL
INFORMATION BASED PENALTY TERM 231 10.2 REGULARIZATION IN STOCHASTIC
POTTS NEURAL NETWORK 237 10.2.1 NEURAL NETWORK ARCHITECTURE 237 10.2.2
SIMULATIONS 239 REFERENCES 243 INDEX 259
|
| any_adam_object | 1 |
| author | Deco, Gustavo 1961- Obradovic, Dragan |
| author_GND | (DE-588)123302803 |
| author_facet | Deco, Gustavo 1961- Obradovic, Dragan |
| author_role | aut aut |
| author_sort | Deco, Gustavo 1961- |
| author_variant | g d gd d o do |
| building | Verbundindex |
| bvnumber | BV010798021 |
| callnumber-first | Q - Science |
| callnumber-label | QA76 |
| callnumber-raw | QA76.87 |
| callnumber-search | QA76.87 |
| callnumber-sort | QA 276.87 |
| callnumber-subject | QA - Mathematics |
| classification_rvk | ST 130 ST 150 ST 152 ST 285 ST 300 ST 301 |
| classification_tum | ELT 505f DAT 717f |
| ctrlnum | (OCoLC)246816567 (DE-599)BVBBV010798021 |
| 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 Elektrotechnik |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02190nam a2200541 c 4500</leader><controlfield tag="001">BV010798021</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20150311 </controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">960617s1996 d||| |||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">0387946667</subfield><subfield code="9">0-387-94666-7</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)246816567</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV010798021</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rakddb</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-20</subfield><subfield code="a">DE-29T</subfield><subfield code="a">DE-91G</subfield><subfield code="a">DE-739</subfield><subfield code="a">DE-19</subfield><subfield code="a">DE-355</subfield><subfield code="a">DE-706</subfield><subfield code="a">DE-634</subfield><subfield code="a">DE-83</subfield><subfield code="a">DE-188</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QA76.87</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">006.3</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 130</subfield><subfield code="0">(DE-625)143588:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 150</subfield><subfield code="0">(DE-625)143594:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 152</subfield><subfield code="0">(DE-625)143596:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 285</subfield><subfield code="0">(DE-625)143648:</subfield><subfield code="2">rvk</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">ST 301</subfield><subfield code="0">(DE-625)143651:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ELT 505f</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">Deco, Gustavo</subfield><subfield code="d">1961-</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)123302803</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">An information-theoretic approach to neural computing</subfield><subfield code="c">Gustavo Deco ; Dragan Obradovic</subfield></datafield><datafield tag="246" ind1="1" ind2="3"><subfield code="a">An information theoretic approach to neural computing</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">New York [u.a.]</subfield><subfield code="b">Springer</subfield><subfield code="c">1996</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">XIII, 261 S.</subfield><subfield code="b">graph. Darst.</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">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Perspectives in neural computing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Neural networks (Computer science)</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Neuronales Netz</subfield><subfield code="0">(DE-588)4226127-2</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Informationstheorie</subfield><subfield code="0">(DE-588)4026927-9</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Neurocomputer</subfield><subfield code="0">(DE-588)4200446-9</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Neuronales Netz</subfield><subfield code="0">(DE-588)4226127-2</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Informationstheorie</subfield><subfield code="0">(DE-588)4026927-9</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="689" ind1="1" ind2="0"><subfield code="a">Neurocomputer</subfield><subfield code="0">(DE-588)4200446-9</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="1" ind2=" "><subfield code="8">1\p</subfield><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Obradovic, Dragan</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">GBV Datenaustausch</subfield><subfield code="q">application/pdf</subfield><subfield code="u">http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=007212561&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA</subfield><subfield code="3">Inhaltsverzeichnis</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-007212561</subfield></datafield><datafield tag="883" ind1="1" ind2=" "><subfield code="8">1\p</subfield><subfield code="a">cgwrk</subfield><subfield code="d">20201028</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#cgwrk</subfield></datafield></record></collection> |
| id | DE-604.BV010798021 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T17:59:03Z |
| institution | BVB |
| isbn | 0387946667 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-007212561 |
| oclc_num | 246816567 |
| open_access_boolean | |
| owner | DE-20 DE-29T DE-91G DE-BY-TUM DE-739 DE-19 DE-BY-UBM DE-355 DE-BY-UBR DE-706 DE-634 DE-83 DE-188 |
| owner_facet | DE-20 DE-29T DE-91G DE-BY-TUM DE-739 DE-19 DE-BY-UBM DE-355 DE-BY-UBR DE-706 DE-634 DE-83 DE-188 |
| physical | XIII, 261 S. graph. Darst. |
| publishDate | 1996 |
| publishDateSearch | 1996 |
| publishDateSort | 1996 |
| publisher | Springer |
| record_format | marc |
| series2 | Perspectives in neural computing |
| spelling | Deco, Gustavo 1961- Verfasser (DE-588)123302803 aut An information-theoretic approach to neural computing Gustavo Deco ; Dragan Obradovic An information theoretic approach to neural computing New York [u.a.] Springer 1996 XIII, 261 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Perspectives in neural computing Neural networks (Computer science) Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Informationstheorie (DE-588)4026927-9 gnd rswk-swf Neurocomputer (DE-588)4200446-9 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 s Informationstheorie (DE-588)4026927-9 s DE-604 Neurocomputer (DE-588)4200446-9 s 1\p DE-604 Obradovic, Dragan Verfasser aut GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=007212561&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Deco, Gustavo 1961- Obradovic, Dragan An information-theoretic approach to neural computing Neural networks (Computer science) Neuronales Netz (DE-588)4226127-2 gnd Informationstheorie (DE-588)4026927-9 gnd Neurocomputer (DE-588)4200446-9 gnd |
| subject_GND | (DE-588)4226127-2 (DE-588)4026927-9 (DE-588)4200446-9 |
| title | An information-theoretic approach to neural computing |
| title_alt | An information theoretic approach to neural computing |
| title_auth | An information-theoretic approach to neural computing |
| title_exact_search | An information-theoretic approach to neural computing |
| title_full | An information-theoretic approach to neural computing Gustavo Deco ; Dragan Obradovic |
| title_fullStr | An information-theoretic approach to neural computing Gustavo Deco ; Dragan Obradovic |
| title_full_unstemmed | An information-theoretic approach to neural computing Gustavo Deco ; Dragan Obradovic |
| title_short | An information-theoretic approach to neural computing |
| title_sort | an information theoretic approach to neural computing |
| topic | Neural networks (Computer science) Neuronales Netz (DE-588)4226127-2 gnd Informationstheorie (DE-588)4026927-9 gnd Neurocomputer (DE-588)4200446-9 gnd |
| topic_facet | Neural networks (Computer science) Neuronales Netz Informationstheorie Neurocomputer |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=007212561&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT decogustavo aninformationtheoreticapproachtoneuralcomputing AT obradovicdragan aninformationtheoreticapproachtoneuralcomputing |