Von dem Buch Comprehensive Analysis of Swarm Based Classifiers and Bayesian Based Models for Epilepsy Risk Level Classification from EEG Signals haben wir 2 gleiche oder sehr ähnliche Ausgaben identifiziert!
Falls Sie nur an einem bestimmten Exempar interessiert sind, können Sie aus der folgenden Liste jenes wählen, an dem Sie interessiert sind:
100%: Harikumar Rajaguru; Sunil Kumar Prabhakar: Comprehensive Analysis of Swarm Based Classifiers and Bayesian Based Models for Epilepsy Risk Level Classification from EEG Signals (ISBN: 9783960676225) 2017, in Englisch, auch als eBook.Nur diese Ausgabe anzeigen…
94%: Harikumar Rajaguru/ Sunil Kumar Prabhakar: Comprehensive Analysis of Swarm Based Classifiers and Bayesian Based Models for Epilepsy Risk Level Classification from EEG Signals (ISBN: 9783960671220) 2017, Anchor Academic Publishing Mrz 2017, in Englisch, Broschiert.Nur diese Ausgabe anzeigen…
Comprehensive Analysis of Swarm Based Classifiers and Bayesian Based Models for Epilepsy Risk Level Classification from EEG Signals
14 Angebote vergleichen
| Preise | März 17 | März 19 | Sep. 19 |
|---|---|---|---|
| Schnitt | € 19,99 | € 20,96 | € 20,90 |
| Nachfrage |
1
Symbolbild
Comprehensive Analysis of Swarm Based Classifiers and Bayesian Based Models for Epilepsy Risk Level Classification from EEG Signals (2017)
DE PB NWISBN: 9783960671220 bzw. 3960671229, in Deutsch, Anchor Academic Publishing Mrz 2017, Taschenbuch, neu.
Lieferung aus: Deutschland, Versandkostenfrei.
Von Händler/Antiquariat, AHA-BUCH GmbH [51283250], Einbeck, Germany.
Neuware - This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO and Bayesian classifier to calculate the epileptic risk level from electroencephalogram (EEG) inputs. PSO is an optimization technique which is initialized with a population of random solutions and searches for optima by updating generations. PSO is initialized with a group of random particles (solutions) and then searches for optima by updating generations. Hybrid PSO differs from ordinary PSO by calculating inertia weight to avoid the local minima problem. Bayesian classifier works on the principle of Bayes' rule in which it is the probability based theorem.The results of PSO, hybrid PSO and Bayesian classifier are calculated and their performance is analyzed using performance index, quality value, cost function and classification rate in calculating the epileptic risk level from EEG. 52 pp. Englisch.
Von Händler/Antiquariat, AHA-BUCH GmbH [51283250], Einbeck, Germany.
Neuware - This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO and Bayesian classifier to calculate the epileptic risk level from electroencephalogram (EEG) inputs. PSO is an optimization technique which is initialized with a population of random solutions and searches for optima by updating generations. PSO is initialized with a group of random particles (solutions) and then searches for optima by updating generations. Hybrid PSO differs from ordinary PSO by calculating inertia weight to avoid the local minima problem. Bayesian classifier works on the principle of Bayes' rule in which it is the probability based theorem.The results of PSO, hybrid PSO and Bayesian classifier are calculated and their performance is analyzed using performance index, quality value, cost function and classification rate in calculating the epileptic risk level from EEG. 52 pp. Englisch.
2
Symbolbild
Comprehensive Analysis of Swarm Based Classifiers and Bayesian Based Models for Epilepsy Risk Level Classification from EEG Signals (2017)
DE PB NWISBN: 9783960671220 bzw. 3960671229, in Deutsch, Anchor Academic Publishing Mrz 2017, Taschenbuch, neu.
Lieferung aus: Deutschland, Versandkostenfrei.
Von Händler/Antiquariat, Agrios-Buch [57449362], Bergisch Gladbach, Germany.
Neuware - This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO and Bayesian classifier to calculate the epileptic risk level from electroencephalogram (EEG) inputs. PSO is an optimization technique which is initialized with a population of random solutions and searches for optima by updating generations. PSO is initialized with a group of random particles (solutions) and then searches for optima by updating generations. Hybrid PSO differs from ordinary PSO by calculating inertia weight to avoid the local minima problem. Bayesian classifier works on the principle of Bayes' rule in which it is the probability based theorem.The results of PSO, hybrid PSO and Bayesian classifier are calculated and their performance is analyzed using performance index, quality value, cost function and classification rate in calculating the epileptic risk level from EEG. 52 pp. Englisch.
Von Händler/Antiquariat, Agrios-Buch [57449362], Bergisch Gladbach, Germany.
Neuware - This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO and Bayesian classifier to calculate the epileptic risk level from electroencephalogram (EEG) inputs. PSO is an optimization technique which is initialized with a population of random solutions and searches for optima by updating generations. PSO is initialized with a group of random particles (solutions) and then searches for optima by updating generations. Hybrid PSO differs from ordinary PSO by calculating inertia weight to avoid the local minima problem. Bayesian classifier works on the principle of Bayes' rule in which it is the probability based theorem.The results of PSO, hybrid PSO and Bayesian classifier are calculated and their performance is analyzed using performance index, quality value, cost function and classification rate in calculating the epileptic risk level from EEG. 52 pp. Englisch.
3
Comprehensive Analysis of Swarm Based Classifiers and Bayesian Based Models for Epilepsy Risk Level Classification from EEG Signals (2017)
~EN NW EBISBN: 9783960676225 bzw. 3960676220, vermutlich in Englisch, Bedey Media GmbH, neu, E-Book.
Lieferung aus: Schweiz, Sofort per Download lieferbar.
This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO and Bayesian classifier to calculate the epileptic risk level from electroencephalogram (EEG) inputs. PSO is an optimization technique which is initialized with a population of random solutions and searches for optima by updating generations. PSO is ... This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO and Bayesian classifier to calculate the epileptic risk level from electroencephalogram (EEG) inputs. PSO is an optimization technique which is initialized with a population of random solutions and searches for optima by updating generations. PSO is initialized with a group of random particles (solutions) and then searches for optima by updating generations. Hybrid PSO differs from ordinary PSO by calculating inertia weight to avoid the local minima problem. Bayesian classifier works on the principle of Bayes´ rule in which it is the probability based theorem. The results of PSO, hybrid PSO and Bayesian classifier are calculated and their performance is analyzed using performance index, quality value, cost function and classification rate in calculating the epileptic risk level from EEG. PDF, 17.02.2017.
This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO and Bayesian classifier to calculate the epileptic risk level from electroencephalogram (EEG) inputs. PSO is an optimization technique which is initialized with a population of random solutions and searches for optima by updating generations. PSO is ... This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO and Bayesian classifier to calculate the epileptic risk level from electroencephalogram (EEG) inputs. PSO is an optimization technique which is initialized with a population of random solutions and searches for optima by updating generations. PSO is initialized with a group of random particles (solutions) and then searches for optima by updating generations. Hybrid PSO differs from ordinary PSO by calculating inertia weight to avoid the local minima problem. Bayesian classifier works on the principle of Bayes´ rule in which it is the probability based theorem. The results of PSO, hybrid PSO and Bayesian classifier are calculated and their performance is analyzed using performance index, quality value, cost function and classification rate in calculating the epileptic risk level from EEG. PDF, 17.02.2017.
4
Comprehensive Analysis of Swarm Based Classifiers and Bayesian Based Models for Epilepsy Risk Level Classification from EEG Signals (2017)
~EN NW EBISBN: 9783960676225 bzw. 3960676220, vermutlich in Englisch, Bedey Media GmbH, neu, E-Book.
Lieferung aus: Deutschland, Sofort per Download lieferbar.
This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO and Bayesian classifier to calculate the epileptic risk level from electroencephalogram (EEG) inputs. PSO is an optimization technique which is initialized with a population of random solutions and searches for optima by updating generations. PSO is ... This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO and Bayesian classifier to calculate the epileptic risk level from electroencephalogram (EEG) inputs. PSO is an optimization technique which is initialized with a population of random solutions and searches for optima by updating generations. PSO is initialized with a group of random particles (solutions) and then searches for optima by updating generations. Hybrid PSO differs from ordinary PSO by calculating inertia weight to avoid the local minima problem. Bayesian classifier works on the principle of Bayes´ rule in which it is the probability based theorem. The results of PSO, hybrid PSO and Bayesian classifier are calculated and their performance is analyzed using performance index, quality value, cost function and classification rate in calculating the epileptic risk level from EEG. 17.02.2017, PDF.
This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO and Bayesian classifier to calculate the epileptic risk level from electroencephalogram (EEG) inputs. PSO is an optimization technique which is initialized with a population of random solutions and searches for optima by updating generations. PSO is ... This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO and Bayesian classifier to calculate the epileptic risk level from electroencephalogram (EEG) inputs. PSO is an optimization technique which is initialized with a population of random solutions and searches for optima by updating generations. PSO is initialized with a group of random particles (solutions) and then searches for optima by updating generations. Hybrid PSO differs from ordinary PSO by calculating inertia weight to avoid the local minima problem. Bayesian classifier works on the principle of Bayes´ rule in which it is the probability based theorem. The results of PSO, hybrid PSO and Bayesian classifier are calculated and their performance is analyzed using performance index, quality value, cost function and classification rate in calculating the epileptic risk level from EEG. 17.02.2017, PDF.
5
Comprehensive Analysis of Swarm Based Classifiers and Bayesian Based Models for Epilepsy Risk Level Classification from EEG Signals (2017)
DE NW EBISBN: 9783960676225 bzw. 3960676220, in Deutsch, Anchor Academic Publishing, neu, E-Book.
This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO and Bayesian classifier to calculate the epileptic risk level from electroencephalogram (EEG) inputs. PSO is an optimization technique which is initialized with a population of random solutions and searches for optima by updating generations. PSO is ... This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO and Bayesian classifier to calculate the epileptic risk level from electroencephalogram (EEG) inputs. PSO is an optimization technique which is initialized with a population of random solutions and searches for optima by updating generations. PSO is initialized with a group of random particles (solutions) and then searches for optima by updating generations. Hybrid PSO differs from ordinary PSO by calculating inertia weight to avoid the local minima problem. Bayesian classifier works on the principle of Bayes rule in which it is the probability based theorem. The results of PSO, hybrid PSO and Bayesian classifier are calculated and their performance is analyzed using performance index, quality value, cost function and classification rate in calculating the epileptic risk level from EEG. 17.02.2017, PDF.
6
Comprehensive Analysis of Swarm Based Classifiers and Bayesian Based Models for Epilepsy Risk Level Classification from EEG Signals (2017)
DE NW EBISBN: 9783960676225 bzw. 3960676220, in Deutsch, Anchor Academic Publishing, neu, E-Book.
Lieferung aus: Schweiz, Sofort per Download lieferbar.
This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO and Bayesian classifier to calculate the epileptic risk level from electroencephalogram (EEG) inputs. PSO is an optimization technique which is initialized with a population of random solutions and searches for optima by updating generations. PSO is ... This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO and Bayesian classifier to calculate the epileptic risk level from electroencephalogram (EEG) inputs. PSO is an optimization technique which is initialized with a population of random solutions and searches for optima by updating generations. PSO is initialized with a group of random particles (solutions) and then searches for optima by updating generations. Hybrid PSO differs from ordinary PSO by calculating inertia weight to avoid the local minima problem. Bayesian classifier works on the principle of Bayes rule in which it is the probability based theorem. The results of PSO, hybrid PSO and Bayesian classifier are calculated and their performance is analyzed using performance index, quality value, cost function and classification rate in calculating the epileptic risk level from EEG. PDF, 17.02.2017.
This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO and Bayesian classifier to calculate the epileptic risk level from electroencephalogram (EEG) inputs. PSO is an optimization technique which is initialized with a population of random solutions and searches for optima by updating generations. PSO is ... This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO and Bayesian classifier to calculate the epileptic risk level from electroencephalogram (EEG) inputs. PSO is an optimization technique which is initialized with a population of random solutions and searches for optima by updating generations. PSO is initialized with a group of random particles (solutions) and then searches for optima by updating generations. Hybrid PSO differs from ordinary PSO by calculating inertia weight to avoid the local minima problem. Bayesian classifier works on the principle of Bayes rule in which it is the probability based theorem. The results of PSO, hybrid PSO and Bayesian classifier are calculated and their performance is analyzed using performance index, quality value, cost function and classification rate in calculating the epileptic risk level from EEG. PDF, 17.02.2017.
7
Comprehensive Analysis of Swarm Based Classifiers and Bayesian Based Models for Epilepsy Risk Level Classification from EEG Signals (eBook, PDF)
DE NW EBISBN: 9783960676225 bzw. 3960676220, in Deutsch, Anchor Academic Publishing, neu, E-Book.
Lieferung aus: Deutschland, Versandkostenfrei innerhalb von Deutschland.
This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO and Bayesian classifier to calculate the epileptic risk level from electroencephalogram (EEG) inputs. PSO is an optimization technique which is initialized with a population of random solutions and searches for optima by updating generations. PSO is initialized with a group of random particles (solutions) and then searches for optima by updating generations. Hybrid PSO differs from ordinary PSO by This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO and Bayesian classifier to calculate the epileptic risk level from electroencephalogram (EEG) inputs. PSO is an optimization technique which is initialized with a population of random solutions and searches for optima by updating generations. PSO is initialized with a group of random particles (solutions) and then searches for optima by updating generations. Hybrid PSO differs from ordinary PSO by calculating inertia weight to avoid the local minima problem. Bayesian classifier works on the principle of Bayes? rule in which it is the probability based theorem.The results of PSO, hybrid PSO and Bayesian classifier are calculated and their performance is analyzed using performance index, quality value, cost function and classification rate in calculating the epileptic risk level from EEG. Lieferzeit 1-2 Werktage.
This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO and Bayesian classifier to calculate the epileptic risk level from electroencephalogram (EEG) inputs. PSO is an optimization technique which is initialized with a population of random solutions and searches for optima by updating generations. PSO is initialized with a group of random particles (solutions) and then searches for optima by updating generations. Hybrid PSO differs from ordinary PSO by This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO and Bayesian classifier to calculate the epileptic risk level from electroencephalogram (EEG) inputs. PSO is an optimization technique which is initialized with a population of random solutions and searches for optima by updating generations. PSO is initialized with a group of random particles (solutions) and then searches for optima by updating generations. Hybrid PSO differs from ordinary PSO by calculating inertia weight to avoid the local minima problem. Bayesian classifier works on the principle of Bayes? rule in which it is the probability based theorem.The results of PSO, hybrid PSO and Bayesian classifier are calculated and their performance is analyzed using performance index, quality value, cost function and classification rate in calculating the epileptic risk level from EEG. Lieferzeit 1-2 Werktage.
8
Comprehensive Analysis of Swarm Based Classifiers and Bayesian Based Models for Epilepsy Risk Level Classification from EEG Signals Harikumar Rajaguru
~EN PB NWISBN: 9783960671220 bzw. 3960671229, vermutlich in Englisch, Anchor Academic Publishing, Taschenbuch, neu.
Lieferung aus: Vereinigte Staaten von Amerika, Lagernd, zzgl. Versandkosten.
This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO and Bayesian classifier to calculate the epileptic risk level from electroencephalogram (EEG) inputs. PSO is an optimization technique which is initialized with a population of random solutions and searches for optima by updating generations. PSO is initialized with a group of random particles (solutions) and then searches for optima by updating generations. Hybrid PSO differs from ordinary PSO by calculating inertia weight to avoid the local minima problem. Bayesian classifier works on the principle of Bayes' rule in which it is the probability based theorem.The results of PSO, hybrid PSO and Bayesian classifier are calculated and their performance is analyzed using performance index, quality value, cost function and classification rate in calculating the epileptic risk level from EEG.
This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO and Bayesian classifier to calculate the epileptic risk level from electroencephalogram (EEG) inputs. PSO is an optimization technique which is initialized with a population of random solutions and searches for optima by updating generations. PSO is initialized with a group of random particles (solutions) and then searches for optima by updating generations. Hybrid PSO differs from ordinary PSO by calculating inertia weight to avoid the local minima problem. Bayesian classifier works on the principle of Bayes' rule in which it is the probability based theorem.The results of PSO, hybrid PSO and Bayesian classifier are calculated and their performance is analyzed using performance index, quality value, cost function and classification rate in calculating the epileptic risk level from EEG.
9
Comprehensive Analysis of Swarm Based Classifiers and Bayesian Based Models for Epilepsy Risk Level Classification from EEG Signals
~EN NW ABISBN: 9783960671220 bzw. 3960671229, vermutlich in Englisch, neu, Hörbuch.
Lieferung aus: Niederlande, Lieferzeit: 5 Tage, zzgl. Versandkosten.
This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO and Bayesian classifier to calculate the epileptic risk level from electroencephalogram (EEG) inputs. PSO is an optimization technique which is initialized with a population of random solutions and searches for optima by updating generations. PSO is initialized with a group of random particles (solutions) and then searches for optima by updating generations. Hybrid PSO differs from ordinary PSO by calculating inertia weight to avoid the local minima problem. Bayesian classifier works on the principle of Bayes' rule in which it is the probability based theorem.The results of PSO, hybrid PSO and Bayesian classifier are calculated and their performance is analyzed using performance index, quality value, cost function and classification rate in calculating the epileptic risk level from EEG.
This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO and Bayesian classifier to calculate the epileptic risk level from electroencephalogram (EEG) inputs. PSO is an optimization technique which is initialized with a population of random solutions and searches for optima by updating generations. PSO is initialized with a group of random particles (solutions) and then searches for optima by updating generations. Hybrid PSO differs from ordinary PSO by calculating inertia weight to avoid the local minima problem. Bayesian classifier works on the principle of Bayes' rule in which it is the probability based theorem.The results of PSO, hybrid PSO and Bayesian classifier are calculated and their performance is analyzed using performance index, quality value, cost function and classification rate in calculating the epileptic risk level from EEG.
10
Comprehensive Analysis of Swarm Based Classifiers and Bayesian Based Models for Epilepsy Risk Level Classification from EEG Signals
DE HC NWISBN: 9783960671220 bzw. 3960671229, in Deutsch, Anchor Academic Publishing, gebundenes Buch, neu.
Lieferung aus: Deutschland, Versandkostenfrei innerhalb von Deutschland.
This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO and Bayesian classifier to calculate the epileptic risk level from electroencephalogram (EEG) inputs. PSO is an optimization technique which is initialized with a population of random solutions and searches for optima by updating generations. PSO is initialized with a group of random particles (solutions) and then searches for optima by updating generations. Hybrid PSO differs from ordinary PSO by This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO and Bayesian classifier to calculate the epileptic risk level from electroencephalogram (EEG) inputs. PSO is an optimization technique which is initialized with a population of random solutions and searches for optima by updating generations. PSO is initialized with a group of random particles (solutions) and then searches for optima by updating generations. Hybrid PSO differs from ordinary PSO by calculating inertia weight to avoid the local minima problem. Bayesian classifier works on the principle of Bayes´ rule in which it is the probability based theorem. The results of PSO, hybrid PSO and Bayesian classifier are calculated and their performance is analyzed using performance index, quality value, cost function and classification rate in calculating the epileptic risk level from EEG. Lieferzeit 1-2 Werktage.
This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO and Bayesian classifier to calculate the epileptic risk level from electroencephalogram (EEG) inputs. PSO is an optimization technique which is initialized with a population of random solutions and searches for optima by updating generations. PSO is initialized with a group of random particles (solutions) and then searches for optima by updating generations. Hybrid PSO differs from ordinary PSO by This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO and Bayesian classifier to calculate the epileptic risk level from electroencephalogram (EEG) inputs. PSO is an optimization technique which is initialized with a population of random solutions and searches for optima by updating generations. PSO is initialized with a group of random particles (solutions) and then searches for optima by updating generations. Hybrid PSO differs from ordinary PSO by calculating inertia weight to avoid the local minima problem. Bayesian classifier works on the principle of Bayes´ rule in which it is the probability based theorem. The results of PSO, hybrid PSO and Bayesian classifier are calculated and their performance is analyzed using performance index, quality value, cost function and classification rate in calculating the epileptic risk level from EEG. Lieferzeit 1-2 Werktage.
Lade…