Ranking of Classifiers Using Active Meta Learning (Paperback)
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| Preise | 2013 | 2014 | 2015 | 2019 |
|---|---|---|---|---|
| Schnitt | Fr. 48.70 (€ 49.81)¹ | Fr. 47.81 (€ 48.91)¹ | Fr. 58.46 (€ 59.80)¹ | Fr. 50.68 (€ 51.84)¹ |
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Ranking of Classifiers Using Active Meta Learning
DE PB NWISBN: 9783659419843 bzw. 3659419842, in Deutsch, LAP LAMBERT Academic Publishing, Taschenbuch, neu.
Von Händler/Antiquariat, BuySomeBooks [52360437], Las Vegas, NV, U.S.A.
Paperback. 108 pages. Dimensions: 8.7in. x 5.9in. x 0.2in.In Classification, Model Selection is one of the critical issues as different models from different categories are available. To select the best model for any given data set is a challenging task. Meta Learning automates this task by acquiring knowledge from the past experience and stores this knowledge into database called Meta Knowledge Base. When new data set comes, stored knowledge can be used for proving ranking of the candidate algorithms. But one of the problems with Meta Learning is generation of Meta Examples as large number of candidate algorithms and data sets are available. To reduce the generation of Meta Examples into Meta Knowledge Base, Active Meta Learning can be used that reduces generation of Meta Examples and at the same time maintaining the performance of candidate algorithms. In this book, Ranking is provided using Active Meta Learning approach by considering Data set Characteristics. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN.
Paperback. 108 pages. Dimensions: 8.7in. x 5.9in. x 0.2in.In Classification, Model Selection is one of the critical issues as different models from different categories are available. To select the best model for any given data set is a challenging task. Meta Learning automates this task by acquiring knowledge from the past experience and stores this knowledge into database called Meta Knowledge Base. When new data set comes, stored knowledge can be used for proving ranking of the candidate algorithms. But one of the problems with Meta Learning is generation of Meta Examples as large number of candidate algorithms and data sets are available. To reduce the generation of Meta Examples into Meta Knowledge Base, Active Meta Learning can be used that reduces generation of Meta Examples and at the same time maintaining the performance of candidate algorithms. In this book, Ranking is provided using Active Meta Learning approach by considering Data set Characteristics. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN.
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Ranking of Classifiers Using Active Meta Learning (Paperback) (2013)
DE PB NW RPISBN: 9783659419843 bzw. 3659419842, in Deutsch, LAP Lambert Academic Publishing, United States, Taschenbuch, neu, Nachdruck.
Lieferung aus: Vereinigtes Königreich Grossbritannien und Nordirland, Versandkostenfrei.
Von Händler/Antiquariat, The Book Depository EURO [60485773], London, United Kingdom.
Language: English Brand New Book ***** Print on Demand *****.In Classification, Model Selection is one of the critical issues as different models from different categories are available. To select the best model for any given data set is a challenging task. Meta Learning automates this task by acquiring knowledge from the past experience and stores this knowledge into database called Meta Knowledge Base. When new data set comes, stored knowledge can be used for proving ranking of the candidate algorithms. But one of the problems with Meta Learning is generation of Meta Examples as large number of candidate algorithms and data sets are available. To reduce the generation of Meta Examples into Meta Knowledge Base, Active Meta Learning can be used that reduces generation of Meta Examples and at the same time maintaining the performance of candidate algorithms. In this book, Ranking is provided using Active Meta Learning approach by considering Data set Characteristics.
Von Händler/Antiquariat, The Book Depository EURO [60485773], London, United Kingdom.
Language: English Brand New Book ***** Print on Demand *****.In Classification, Model Selection is one of the critical issues as different models from different categories are available. To select the best model for any given data set is a challenging task. Meta Learning automates this task by acquiring knowledge from the past experience and stores this knowledge into database called Meta Knowledge Base. When new data set comes, stored knowledge can be used for proving ranking of the candidate algorithms. But one of the problems with Meta Learning is generation of Meta Examples as large number of candidate algorithms and data sets are available. To reduce the generation of Meta Examples into Meta Knowledge Base, Active Meta Learning can be used that reduces generation of Meta Examples and at the same time maintaining the performance of candidate algorithms. In this book, Ranking is provided using Active Meta Learning approach by considering Data set Characteristics.
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Ranking of Classifiers Using Active Meta Learning
~EN NW ABISBN: 9783659419843 bzw. 3659419842, vermutlich in Englisch, neu, Hörbuch.
Lieferung aus: Niederlande, Lieferzeit: 5 Tage, zzgl. Versandkosten.
In Classification, Model Selection is one of the critical issues as different models from different categories are available. To select the best model for any given data set is a challenging task. Meta Learning automates this task by acquiring knowledge from the past experience and stores this knowledge into database called Meta Knowledge Base. When new data set comes, stored knowledge can be used for proving ranking of the candidate algorithms. But one of the problems with Meta Learning is generation of Meta Examples as large number of candidate algorithms and data sets are available. To reduce the generation of Meta Examples into Meta Knowledge Base, Active Meta Learning can be used that reduces generation of Meta Examples and at the same time maintaining the performance of candidate algorithms. In this book, Ranking is provided using Active Meta Learning approach by considering Data set Characteristics.
In Classification, Model Selection is one of the critical issues as different models from different categories are available. To select the best model for any given data set is a challenging task. Meta Learning automates this task by acquiring knowledge from the past experience and stores this knowledge into database called Meta Knowledge Base. When new data set comes, stored knowledge can be used for proving ranking of the candidate algorithms. But one of the problems with Meta Learning is generation of Meta Examples as large number of candidate algorithms and data sets are available. To reduce the generation of Meta Examples into Meta Knowledge Base, Active Meta Learning can be used that reduces generation of Meta Examples and at the same time maintaining the performance of candidate algorithms. In this book, Ranking is provided using Active Meta Learning approach by considering Data set Characteristics.
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Ranking of Classifiers Using Active Meta Learning
~EN PB NWISBN: 9783659419843 bzw. 3659419842, vermutlich in Englisch, LAP Lambert Academic Publishing, Taschenbuch, neu.
Lieferung aus: Deutschland, Versandkostenfrei.
Ranking of Classifiers Using Active Meta Learning: In Classification, Model Selection is one of the critical issues as different models from different categories are available. To select the best model for any given data set is a challenging task. Meta Learning automates this task by acquiring knowledge from the past experience and stores this knowledge into database called Meta Knowledge Base. When new data set comes, stored knowledge can be used for proving ranking of the candidate algorithms. But one of the problems with Meta Learning is generation of Meta Examples as large number of candidate algorithms and data sets are available. To reduce the generation of Meta Examples into Meta Knowledge Base, Active Meta Learning can be used that reduces generation of Meta Examples and at the same time maintaining the performance of candidate algorithms. In this book, Ranking is provided using Active Meta Learning approach by considering Data set Characteristics. Englisch, Taschenbuch.
Ranking of Classifiers Using Active Meta Learning: In Classification, Model Selection is one of the critical issues as different models from different categories are available. To select the best model for any given data set is a challenging task. Meta Learning automates this task by acquiring knowledge from the past experience and stores this knowledge into database called Meta Knowledge Base. When new data set comes, stored knowledge can be used for proving ranking of the candidate algorithms. But one of the problems with Meta Learning is generation of Meta Examples as large number of candidate algorithms and data sets are available. To reduce the generation of Meta Examples into Meta Knowledge Base, Active Meta Learning can be used that reduces generation of Meta Examples and at the same time maintaining the performance of candidate algorithms. In this book, Ranking is provided using Active Meta Learning approach by considering Data set Characteristics. Englisch, Taschenbuch.
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Ranking of Classifiers Using Active Meta Learning (2015)
DE PB NWISBN: 9783659419843 bzw. 3659419842, in Deutsch, LAP LAMBERT ACADEMIC PUB 01/06/2015, Taschenbuch, neu.
Von Händler/Antiquariat, Books2Anywhere [190245], Swindon, United Kingdom.
New Book. Shipped from UK in 4 to 14 days. Established seller since 2000. This item is printed on demand.
New Book. Shipped from UK in 4 to 14 days. Established seller since 2000. This item is printed on demand.
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Ranking of Classifiers Using Active Meta Learning (2014)
DE PB NWISBN: 9783659419843 bzw. 3659419842, in Deutsch, LAP LAMBERT ACADEMIC PUB 01/07/2014, Taschenbuch, neu.
Von Händler/Antiquariat, Paperbackshop-US [8408184], Secaucus, NJ, U.S.A.
New Book. This item is printed on demand. Shipped from US This item is printed on demand.
New Book. This item is printed on demand. Shipped from US This item is printed on demand.
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Ranking of Classifiers Using Active Meta Learning (2013)
DE PB NW RPISBN: 9783659419843 bzw. 3659419842, in Deutsch, LAP Lambert Academic Publishing, Taschenbuch, neu, Nachdruck.
Von Händler/Antiquariat, English-Book-Service - A Fine Choice [1048135], Waldshut-Tiengen, Germany.
This item is printed on demand for shipment within 3 working days.
This item is printed on demand for shipment within 3 working days.
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