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LEADER |
03129aam a22004331i 4500 |
001 |
180-018659739 |
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Uk |
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20190208204442.0 |
006 |
m || d | |
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cr ||||||||||| |
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171223s2017 |||||||fo |||| ||eng |
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|2 bnb
|
020 |
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|a 9781351835558
|q EPUB
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|
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|a 1351835556
|q EPUB
|
037 |
|
|
|a 9781351835558
|b Ingram Content Group
|
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|
|
|a Uk
|b eng
|c Uk
|
042 |
|
|
|a ukblsr
|
082 |
0 |
0 |
|a 006.4
|2 21
|
100 |
1 |
|
|a Micheli-Tzanakou, Evangelia
|d 1942-
|
245 |
1 |
0 |
|a Supervised and unsupervised pattern recognition
|b feature extraction and computational intelligence
|c [edited by] Evangelia Micheli-Tzanakou
|
264 |
|
1 |
|a [Place of publication not identified]
|b CRC Press
|c 2017
|
300 |
|
|
|a 1 online resource (392 pages :
|b 9 illustrations).
|
336 |
|
|
|a text
|2 rdacontent
|
337 |
|
|
|a computer
|2 rdamedia
|
338 |
|
|
|a online resource
|2 rdacarrier
|
490 |
1 |
|
|a Industrial electronics series
|
505 |
0 |
0 |
|t Overviews of Neural Networks, Classifiers, and Feature Extraction Methods -- Supervised Neural Networks --
|t Criteria for Optimal Classifier Design --
|t Categorizing the Classifiers --
|t Bayesian Optimal Classifiers --
|t Exemplar Classifiers --
|t Space Partition Methods --
|t Neural Networks --
|t Bayesian Classifiers --
|t Minimum ECM Classifers --
|t Multi-Class Optimal Classifiers --
|t Bayesian Classifiers with Multivariate Normal Populations --
|t Quadratic Discriminant Score --
|t Linear Discriminant Score --
|t Linear Discriminant Analysis and Classification --
|t Equivalence of LDF to Minimum TPM Classifier --
|t Learning Vector Quantizer (LVQ) --
|t Competitive Learning --
|t Self-Organizing Map --
|t Learning Vector Quantization --
|t Nearest Neighbor Rule --
|t Neural Networks (NN) --
|t Artificial Neural Networks --
|t Usage of Neural Networks --
|t Other Neural Networks --
|t Feed-Forward Neural Networks --
|t Error Backpropagation --
|t Madaline Rule III for Multilayer Network with Sigmoid Function --
|t A Comment on the Terminology 'Backpropagation' --
|t Optimization Machines with Feed-Forward Multilayer Perceptrons --
|t Justification for Gradient Methods for Nonlinear Function Approximation --
|t Training Methods for Feed-Forward Networks --
|t Issues in Neural Networks --
|t Universal Approximation --
|t Enhancing Convergence Rate and Generalization of an Optimization Machine --
|t Suggestions for Improving the Convergence --
|t Quick Prop --
|t Kullback-Leibler Distance --
|t Weight Decay --
|t Regression Methods for Classification Purposes --
|t Two-Group Regression and Linear Discriminant Function.
|
650 |
|
0 |
|a Pattern recognition systems.
|
650 |
|
0 |
|a Neural networks (Computer science)
|
650 |
|
7 |
|a Neural networks (Computer science)
|2 fast
|0 (OCoLC)fst01036260
|
650 |
|
7 |
|a Pattern recognition systems.
|2 fast
|0 (OCoLC)fst01055266
|
650 |
|
7 |
|a Mustererkennung
|2 gnd
|
650 |
|
7 |
|a Neuronales Netz
|2 gnd
|
650 |
|
7 |
|a Reconnaissance des formes (informatique)
|2 ram
|
650 |
|
7 |
|a Réseaux neuronaux (informatique)
|2 ram
|
650 |
0 |
7 |
|a Mustererkennung.
|2 swd
|
830 |
|
0 |
|a Industrial electronics series.
|
859 |
|
|
|a ELD
|b ebook
|
980 |
|
|
|a 018659739
|b 180
|c sid-180-col-bnbfidbbi
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SOLR
_version_ |
1778756676428496896 |
access_facet |
Electronic Resources |
author |
Micheli-Tzanakou, Evangelia |
author_facet |
Micheli-Tzanakou, Evangelia |
author_role |
|
author_sort |
Micheli-Tzanakou, Evangelia 1942- |
author_variant |
e m t emt |
building |
Library A |
callnumber-sort |
|
collection |
sid-180-col-bnbfidbbi |
contents |
Overviews of Neural Networks, Classifiers, and Feature Extraction Methods -- Supervised Neural Networks -- Criteria for Optimal Classifier Design -- Categorizing the Classifiers -- Bayesian Optimal Classifiers -- Exemplar Classifiers -- Space Partition Methods -- Neural Networks -- Bayesian Classifiers -- Minimum ECM Classifers -- Multi-Class Optimal Classifiers -- Bayesian Classifiers with Multivariate Normal Populations -- Quadratic Discriminant Score -- Linear Discriminant Score -- Linear Discriminant Analysis and Classification -- Equivalence of LDF to Minimum TPM Classifier -- Learning Vector Quantizer (LVQ) -- Competitive Learning -- Self-Organizing Map -- Learning Vector Quantization -- Nearest Neighbor Rule -- Neural Networks (NN) -- Artificial Neural Networks -- Usage of Neural Networks -- Other Neural Networks -- Feed-Forward Neural Networks -- Error Backpropagation -- Madaline Rule III for Multilayer Network with Sigmoid Function -- A Comment on the Terminology 'Backpropagation' -- Optimization Machines with Feed-Forward Multilayer Perceptrons -- Justification for Gradient Methods for Nonlinear Function Approximation -- Training Methods for Feed-Forward Networks -- Issues in Neural Networks -- Universal Approximation -- Enhancing Convergence Rate and Generalization of an Optimization Machine -- Suggestions for Improving the Convergence -- Quick Prop -- Kullback-Leibler Distance -- Weight Decay -- Regression Methods for Classification Purposes -- Two-Group Regression and Linear Discriminant Function. |
dewey-full |
006.4 |
dewey-hundreds |
000 - Computer science, information & general works |
dewey-ones |
006 - Special computer methods |
dewey-raw |
006.4 |
dewey-search |
006.4 |
dewey-sort |
16.4 |
dewey-tens |
000 - Computer science, knowledge & systems |
facet_avail |
Online |
finc_class_facet |
Informatik |
fincclass_txtF_mv |
science-computerscience |
format |
eBook |
format_access_txtF_mv |
Book, E-Book |
format_de105 |
Ebook |
format_de14 |
Book, E-Book |
format_de15 |
Book, E-Book |
format_del152 |
Buch |
format_detail_txtF_mv |
text-online-monograph-independent |
format_dezi4 |
e-Book |
format_finc |
Book, E-Book |
format_legacy |
ElectronicBook |
format_legacy_nrw |
Book, E-Book |
format_nrw |
Book, E-Book |
format_strict_txtF_mv |
E-Book |
geogr_code |
not assigned |
geogr_code_person |
not assigned |
id |
180-018659739 |
illustrated |
Not Illustrated |
imprint |
[Place of publication not identified], CRC Press, 2017 |
imprint_str_mv |
[Place of publication not identified] CRC Press 2017 |
institution |
FID-BBI-DE-23 |
is_hierarchy_id |
|
is_hierarchy_title |
|
isbn |
9781351835558, 1351835556 |
isil_str_mv |
FID-BBI-DE-23 |
language |
English |
last_indexed |
2023-10-03T17:35:56.952Z |
match_str |
michelitzanakou2017supervisedandunsupervisedpatternrecognitionfeatureextractionandcomputationalintelligence |
mega_collection |
British National Bibliography |
physical |
1 online resource (392 pages; 9 illustrations) |
publishDate |
2017 |
publishDateSort |
2017 |
publishPlace |
[Place of publication not identified] |
publisher |
CRC Press |
record_format |
marcfinc |
record_id |
018659739 |
recordtype |
marcfinc |
rvk_facet |
No subject assigned |
series |
Industrial electronics series |
series2 |
Industrial electronics series |
source_id |
180 |
spelling |
Micheli-Tzanakou, Evangelia 1942-, Supervised and unsupervised pattern recognition feature extraction and computational intelligence [edited by] Evangelia Micheli-Tzanakou, [Place of publication not identified] CRC Press 2017, 1 online resource (392 pages : 9 illustrations)., text rdacontent, computer rdamedia, online resource rdacarrier, Industrial electronics series, Overviews of Neural Networks, Classifiers, and Feature Extraction Methods -- Supervised Neural Networks -- Criteria for Optimal Classifier Design -- Categorizing the Classifiers -- Bayesian Optimal Classifiers -- Exemplar Classifiers -- Space Partition Methods -- Neural Networks -- Bayesian Classifiers -- Minimum ECM Classifers -- Multi-Class Optimal Classifiers -- Bayesian Classifiers with Multivariate Normal Populations -- Quadratic Discriminant Score -- Linear Discriminant Score -- Linear Discriminant Analysis and Classification -- Equivalence of LDF to Minimum TPM Classifier -- Learning Vector Quantizer (LVQ) -- Competitive Learning -- Self-Organizing Map -- Learning Vector Quantization -- Nearest Neighbor Rule -- Neural Networks (NN) -- Artificial Neural Networks -- Usage of Neural Networks -- Other Neural Networks -- Feed-Forward Neural Networks -- Error Backpropagation -- Madaline Rule III for Multilayer Network with Sigmoid Function -- A Comment on the Terminology 'Backpropagation' -- Optimization Machines with Feed-Forward Multilayer Perceptrons -- Justification for Gradient Methods for Nonlinear Function Approximation -- Training Methods for Feed-Forward Networks -- Issues in Neural Networks -- Universal Approximation -- Enhancing Convergence Rate and Generalization of an Optimization Machine -- Suggestions for Improving the Convergence -- Quick Prop -- Kullback-Leibler Distance -- Weight Decay -- Regression Methods for Classification Purposes -- Two-Group Regression and Linear Discriminant Function., Pattern recognition systems., Neural networks (Computer science), Neural networks (Computer science) fast (OCoLC)fst01036260, Pattern recognition systems. fast (OCoLC)fst01055266, Mustererkennung gnd, Neuronales Netz gnd, Reconnaissance des formes (informatique) ram, Réseaux neuronaux (informatique) ram, Mustererkennung. swd, Industrial electronics series., ELD ebook |
spellingShingle |
Micheli-Tzanakou, Evangelia, Supervised and unsupervised pattern recognition: feature extraction and computational intelligence, Industrial electronics series, Overviews of Neural Networks, Classifiers, and Feature Extraction Methods -- Supervised Neural Networks -- Criteria for Optimal Classifier Design -- Categorizing the Classifiers -- Bayesian Optimal Classifiers -- Exemplar Classifiers -- Space Partition Methods -- Neural Networks -- Bayesian Classifiers -- Minimum ECM Classifers -- Multi-Class Optimal Classifiers -- Bayesian Classifiers with Multivariate Normal Populations -- Quadratic Discriminant Score -- Linear Discriminant Score -- Linear Discriminant Analysis and Classification -- Equivalence of LDF to Minimum TPM Classifier -- Learning Vector Quantizer (LVQ) -- Competitive Learning -- Self-Organizing Map -- Learning Vector Quantization -- Nearest Neighbor Rule -- Neural Networks (NN) -- Artificial Neural Networks -- Usage of Neural Networks -- Other Neural Networks -- Feed-Forward Neural Networks -- Error Backpropagation -- Madaline Rule III for Multilayer Network with Sigmoid Function -- A Comment on the Terminology 'Backpropagation' -- Optimization Machines with Feed-Forward Multilayer Perceptrons -- Justification for Gradient Methods for Nonlinear Function Approximation -- Training Methods for Feed-Forward Networks -- Issues in Neural Networks -- Universal Approximation -- Enhancing Convergence Rate and Generalization of an Optimization Machine -- Suggestions for Improving the Convergence -- Quick Prop -- Kullback-Leibler Distance -- Weight Decay -- Regression Methods for Classification Purposes -- Two-Group Regression and Linear Discriminant Function., Pattern recognition systems., Neural networks (Computer science), Mustererkennung, Neuronales Netz, Reconnaissance des formes (informatique), Réseaux neuronaux (informatique), Mustererkennung. |
title |
Supervised and unsupervised pattern recognition: feature extraction and computational intelligence |
title_alt |
Overviews of Neural Networks, Classifiers, and Feature Extraction Methods -- Supervised Neural Networks --, Criteria for Optimal Classifier Design --, Categorizing the Classifiers --, Bayesian Optimal Classifiers --, Exemplar Classifiers --, Space Partition Methods --, Neural Networks --, Bayesian Classifiers --, Minimum ECM Classifers --, Multi-Class Optimal Classifiers --, Bayesian Classifiers with Multivariate Normal Populations --, Quadratic Discriminant Score --, Linear Discriminant Score --, Linear Discriminant Analysis and Classification --, Equivalence of LDF to Minimum TPM Classifier --, Learning Vector Quantizer (LVQ) --, Competitive Learning --, Self-Organizing Map --, Learning Vector Quantization --, Nearest Neighbor Rule --, Neural Networks (NN) --, Artificial Neural Networks --, Usage of Neural Networks --, Other Neural Networks --, Feed-Forward Neural Networks --, Error Backpropagation --, Madaline Rule III for Multilayer Network with Sigmoid Function --, A Comment on the Terminology 'Backpropagation' --, Optimization Machines with Feed-Forward Multilayer Perceptrons --, Justification for Gradient Methods for Nonlinear Function Approximation --, Training Methods for Feed-Forward Networks --, Issues in Neural Networks --, Universal Approximation --, Enhancing Convergence Rate and Generalization of an Optimization Machine --, Suggestions for Improving the Convergence --, Quick Prop --, Kullback-Leibler Distance --, Weight Decay --, Regression Methods for Classification Purposes --, Two-Group Regression and Linear Discriminant Function. |
title_auth |
Supervised and unsupervised pattern recognition feature extraction and computational intelligence |
title_full |
Supervised and unsupervised pattern recognition feature extraction and computational intelligence [edited by] Evangelia Micheli-Tzanakou |
title_fullStr |
Supervised and unsupervised pattern recognition feature extraction and computational intelligence [edited by] Evangelia Micheli-Tzanakou |
title_full_unstemmed |
Supervised and unsupervised pattern recognition feature extraction and computational intelligence [edited by] Evangelia Micheli-Tzanakou |
title_in_hierarchy |
Supervised and unsupervised pattern recognition: feature extraction and computational intelligence (2017) |
title_short |
Supervised and unsupervised pattern recognition |
title_sort |
supervised and unsupervised pattern recognition feature extraction and computational intelligence |
title_sub |
feature extraction and computational intelligence |
topic |
Pattern recognition systems., Neural networks (Computer science), Mustererkennung, Neuronales Netz, Reconnaissance des formes (informatique), Réseaux neuronaux (informatique), Mustererkennung. |
topic_facet |
Pattern recognition systems., Neural networks (Computer science), Mustererkennung, Neuronales Netz, Reconnaissance des formes (informatique), Réseaux neuronaux (informatique), Mustererkennung. |