Oracle Data Mining Implementation by Cupet Victoria (6 results)
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Seller: preigu, Osnabrück, Germanypreigu
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Taschenbuch. Condition: Neu. Oracle Data Mining and the implementation of Support Vector Machine | Victoria Cupet | Taschenbuch | 88 S. | Englisch | 2015 | LAP LAMBERT Academic Publishing | EAN 9783659716973 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de… | Anbieter: preigu.
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Seller: Buchpark, Trebbin, GermanyBuchpark
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Condition: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | Contemporary commercial databases are putting an increased accentuation on analytic abilities. The data mining technology is very important when we are referring to large volumes of data for analysis. Regarding novel data if we use modern data mini…ng techniques we may improve their accuracy and generalization. But as we all know attaining results of good quality frequently demands high level of proficiency and user expertise. Support Vector Machines is a wonderful and potent state-of-the-art data mining algorithm and can express problems not compliant to traditional statistical analysis. Anywise, this kind of algorithm stays limited on the strength of methodological complexities, scalability challenges, and scarcity of production quality SVM implementations. The paper hereby portrays Oracle¿s implementation of SVM where the primary topic lies on ease of use and scalability whilst maintaining high performance accuracy. Support Vector Machines algorithm is entirely integrated into the Oracle database framework and so it can be easily leveraged in a multifariousness of deployment scenarios.
Language: English
Published by LAP Lambert Academic Publishing Jun 2015, 2015
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Contemporary commercial databases are putting an increased accentuation on analytic abilities. The data mining technology is very important when we are referring to large volumes of data for analysis. Regarding novel data if we use…modern data mining techniques we may improve their accuracy and generalization. But as we all know attaining results of good quality frequently demands high level of proficiency and user expertise. Support Vector Machines is a wonderful and potent state-of-the-art data mining algorithm and can express problems not compliant to traditional statistical analysis. Anywise, this kind of algorithm stays limited on the strength of methodological complexities, scalability challenges, and scarcity of production quality SVM implementations. The paper hereby portrays Oracle's implementation of SVM where the primary topic lies on ease of use and scalability whilst maintaining high performance accuracy. Support Vector Machines algorithm is entirely integrated into the Oracle database framework and so it can be easily leveraged in a multifariousness of deployment scenarios. 88 pp. Englisch.
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Seller: moluna, Greven, Germanymoluna
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Cupet VictoriaVictoria Cupet is a consultant, trainer and coach with an experience of more than 10 years in such fields as Business Analysis, Process Management, Project Managements and Agile.Contempora…ry commercial databases are.
Language: English
Published by LAP LAMBERT Academic Publishing Jun 2015, 2015
- Softcover
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Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Contemporary commercial databases are putting an increased accentuation on analytic abilities. The data mining technology is very important when we are referring to large volumes of data for analysis. Regarding novel data if we use mode…rn data mining techniques we may improve their accuracy and generalization. But as we all know attaining results of good quality frequently demands high level of proficiency and user expertise. Support Vector Machines is a wonderful and potent state-of-the-art data mining algorithm and can express problems not compliant to traditional statistical analysis. Anywise, this kind of algorithm stays limited on the strength of methodological complexities, scalability challenges, and scarcity of production quality SVM implementations. The paper hereby portrays Oracle¿s implementation of SVM where the primary topic lies on ease of use and scalability whilst maintaining high performance accuracy. Support Vector Machines algorithm is entirely integrated into the Oracle database framework and so it can be easily leveraged in a multifariousness of deployment scenarios.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 88 pp. Englisch.
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Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Contemporary commercial databases are putting an increased accentuation on analytic abilities. The data mining technology is very important when we are referring to large volumes of data for analysis. Regarding novel data if we use moder…n data mining techniques we may improve their accuracy and generalization. But as we all know attaining results of good quality frequently demands high level of proficiency and user expertise. Support Vector Machines is a wonderful and potent state-of-the-art data mining algorithm and can express problems not compliant to traditional statistical analysis. Anywise, this kind of algorithm stays limited on the strength of methodological complexities, scalability challenges, and scarcity of production quality SVM implementations. The paper hereby portrays Oracle's implementation of SVM where the primary topic lies on ease of use and scalability whilst maintaining high performance accuracy. Support Vector Machines algorithm is entirely integrated into the Oracle database framework and so it can be easily leveraged in a multifariousness of deployment scenarios.
