Librería: Phatpocket Limited, Waltham Abbey, HERTS, Reino Unido
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Librería: Bahamut Media, Reading, Reino Unido
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Librería: AwesomeBooks, Wallingford, Reino Unido
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Añadir al carritoHardcover. Condición: Very Good. Text Mining: Predictive Methods for Analyzing Unstructured Information This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping. .
Librería: SecondSale, Montgomery, IL, Estados Unidos de America
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Librería: BennettBooksLtd, North Las Vegas, NV, Estados Unidos de America
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Añadir al carritoHardcover. Condición: new. Excellent Condition.Excels in customer satisfaction, prompt replies, and quality checks.
Librería: Mispah books, Redhill, SURRE, Reino Unido
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Librería: Ria Christie Collections, Uxbridge, Reino Unido
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Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 162,91
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Añadir al carritoTaschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Data mining is a mature technology. The prediction problem, looking for predictive patterns in data, has been widely studied. Strong me- ods are available to the practitioner. These methods process structured numerical information, where uniform measurements are taken over a sample of data. Text is often described as unstructured information. So, it would seem, text and numerical data are different, requiring different methods. Or are they In our view, a prediction problem can be solved by the same methods, whether the data are structured - merical measurements or unstructured text. Text and documents can be transformed into measured values, such as the presence or absence of words, and the same methods that have proven successful for pred- tive data mining can be applied to text. Yet, there are key differences. Evaluation techniques must be adapted to the chronological order of publication and to alternative measures of error. Because the data are documents, more specialized analytical methods may be preferred for text. Moreover, the methods must be modi ed to accommodate very high dimensions: tens of thousands of words and documents. Still, the central themes are similar.
Librería: Aragon Books Canada, OTTAWA, ON, Canada
EUR 127,30
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Librería: Lucky's Textbooks, Dallas, TX, Estados Unidos de America
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Librería: Lucky's Textbooks, Dallas, TX, Estados Unidos de America
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Librería: HPB-Red, Dallas, TX, Estados Unidos de America
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Añadir al carritopaperback. Condición: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
Librería: Best Price, Torrance, CA, Estados Unidos de America
EUR 149,26
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Librería: Best Price, Torrance, CA, Estados Unidos de America
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Librería: Mispah books, Redhill, SURRE, Reino Unido
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Añadir al carritoPaperback. Condición: Like New. Like New. book.
Publicado por Springer New York Okt 2004, 2004
ISBN 10: 0387954333 ISBN 13: 9780387954332
Idioma: Inglés
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 247,94
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Añadir al carritoBuch. Condición: Neu. Neuware - Data mining is a mature technology. The prediction problem, looking for predictive patterns in data, has been widely studied. Strong me- ods are available to the practitioner. These methods process structured numerical information, where uniform measurements are taken over a sample of data. Text is often described as unstructured information. So, it would seem, text and numerical data are different, requiring different methods. Or are they In our view, a prediction problem can be solved by the same methods, whether the data are structured - merical measurements or unstructured text. Text and documents can be transformed into measured values, such as the presence or absence of words, and the same methods that have proven successful for pred- tive data mining can be applied to text. Yet, there are key differences. Evaluation techniques must be adapted to the chronological order of publication and to alternative measures of error. Because the data are documents, more specialized analytical methods may be preferred for text. Moreover, the methods must be modi ed to accommodate very high dimensions: tens of thousands of words and documents. Still, the central themes are similar.