Semenenko nataliia (8 resultados)
Idioma: Inglés
Editorial: VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2014
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Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
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EUR 65,28
Envío por EUR 3,50Se envía dentro de Estados Unidos de AmericaCantidad disponible: 4 disponibles
Condición: New. pp. 68.
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Librería: preigu, Osnabrück, Alemaniapreigu
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EUR 36,35
Envío por EUR 70,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 5 disponibles
Taschenbuch. Condición: Neu. Diagnosis of Cross-Browser Compatibility Issues via Machine Learning | Nataliia Semenenko | Taschenbuch | 68 S. | Englisch | 2014 | LAP LAMBERT Academic Publishing | EAN 9783659185564 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[d…ot]de | Anbieter: preigu.
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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EUR 39,90
Envío por EUR 23,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 2 disponibles
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Due to the rapid evolution of Web technologies and the failure of Web standards to uniformize every single technology evolution, Web developers are faced with the challenge of ensuring that their applications are correctly rendered…across a broad range of browsers and platforms. To detect cross-browser incompatibilities, developers often resort to visually checking that each document produced by their application is consistently rendered across all relevant browsers. This manual testing approach is time consuming and error-prone. Existing cross-browser compatibility testing tools speed up this process. However, existing tools in this space suffer from over-sensitivity. Reducing the number of false positives produced by these testing tools is challenging, since defining criteria for classifying a difference as an incompatibility is to some extent subjective. This work presents a machine learning approach to improve the accuracy of two techniques for cross-browser compatibility testing - one based on image analysis and one based on DOM analysis. Two classification algorithms were used, namely classification trees and neural networks. 68 pp. Englisch.
Idioma: Inglés
Editorial: VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2014
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Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
Contactar con el vendedorVendedor de 4 estrellasCondición: Nuevo
EUR 65,25
Envío por EUR 7,62Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 4 disponibles
Condición: New. Print on Demand pp. 68 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.
Idioma: Inglés
Editorial: VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2014
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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
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EUR 65,69
Envío por EUR 9,95Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 4 disponibles
Condición: New. PRINT ON DEMAND pp. 68.
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Librería: moluna, Greven, Alemaniamoluna
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 34,25
Envío por EUR 48,99Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Semenenko NataliiaNataliia started her studies in NTUU KPI , Kyiv, Ukraine. After completing her bachelor degree in Computer Science she moved to Estonia for Software Engineering Master programme in Tar…tu University which she succes.
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 39,90
Envío por EUR 60,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Due to the rapid evolution of Web technologies and the failure of Web standards to uniformize every single technology evolution, Web developers are faced with the challenge of ensuring that their applications are correctly rendered acro…ss a broad range of browsers and platforms. To detect cross-browser incompatibilities, developers often resort to visually checking that each document produced by their application is consistently rendered across all relevant browsers. This manual testing approach is time consuming and error-prone. Existing cross-browser compatibility testing tools speed up this process. However, existing tools in this space suffer from over-sensitivity. Reducing the number of false positives produced by these testing tools is challenging, since defining criteria for classifying a difference as an incompatibility is to some extent subjective. This work presents a machine learning approach to improve the accuracy of two techniques for cross-browser compatibility testing ¿ one based on image analysis and one based on DOM analysis. Two classification algorithms were used, namely classification trees and neural networks.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 68 pp. Englisch.
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 40,89
Envío por EUR 60,60Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Due to the rapid evolution of Web technologies and the failure of Web standards to uniformize every single technology evolution, Web developers are faced with the challenge of ensuring that their applications are correctly rendered acros…s a broad range of browsers and platforms. To detect cross-browser incompatibilities, developers often resort to visually checking that each document produced by their application is consistently rendered across all relevant browsers. This manual testing approach is time consuming and error-prone. Existing cross-browser compatibility testing tools speed up this process. However, existing tools in this space suffer from over-sensitivity. Reducing the number of false positives produced by these testing tools is challenging, since defining criteria for classifying a difference as an incompatibility is to some extent subjective. This work presents a machine learning approach to improve the accuracy of two techniques for cross-browser compatibility testing - one based on image analysis and one based on DOM analysis. Two classification algorithms were used, namely classification trees and neural networks.
