Artículos relacionados a Automating the Design of Data Mining Algorithms: An...

Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach (Natural Computing Series) - Tapa dura

 
9783642025402: Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach (Natural Computing Series)

Sinopsis

Data mining is a very active research area with many successful real-world app- cations. It consists of a set of concepts and methods used to extract interesting or useful knowledge (or patterns) from real-world datasets, providing valuable support for decision making in industry, business, government, and science. Although there are already many types of data mining algorithms available in the literature, it is still dif cult for users to choose the best possible data mining algorithm for their particular data mining problem. In addition, data mining al- rithms have been manually designed; therefore they incorporate human biases and preferences. This book proposes a new approach to the design of data mining algorithms. - stead of relying on the slow and ad hoc process of manual algorithm design, this book proposes systematically automating the design of data mining algorithms with an evolutionary computation approach. More precisely, we propose a genetic p- gramming system (a type of evolutionary computation method that evolves c- puter programs) to automate the design of rule induction algorithms, a type of cl- si cation method that discovers a set of classi cation rules from data. We focus on genetic programming in this book because it is the paradigmatic type of machine learning method for automating the generation of programs and because it has the advantage of performing a global search in the space of candidate solutions (data mining algorithms in our case), but in principle other types of search methods for this task could be investigated in the future.

"Sinopsis" puede pertenecer a otra edición de este libro.

Críticas

From the reviews:

"The book is targeted at researchers and postgraduate students. As the amount of data being mined continues to grow it demands ever more sophisticated mining algorithms. Therefore there is a need for new algorithms and so Pappa and Freitas’ book will be of interest particularly to researchers in data mining. ... [T]his book will appeal to the target audience of [the journal] Genetic Programming and Evolvable Machines and, I feel, will align with the research interests of its readership." (John Woodward, Genetic Programming and Evolvable Machines (2011) 12:81–83)

“The book will be useful for postgraduate students and researchers in the data mining field and in evolutionary computation.” (Florin Gorunescu, Zentralblatt MATH, Vol. 1183, 2010)

Reseña del editor

Data mining is a very active research area with many successful real-world app- cations. It consists of a set of concepts and methods used to extract interesting or useful knowledge (or patterns) from real-world datasets, providing valuable support for decision making in industry, business, government, and science. Although there are already many types of data mining algorithms available in the literature, it is still dif cult for users to choose the best possible data mining algorithm for their particular data mining problem. In addition, data mining al- rithms have been manually designed; therefore they incorporate human biases and preferences. This book proposes a new approach to the design of data mining algorithms. - stead of relying on the slow and ad hoc process of manual algorithm design, this book proposes systematically automating the design of data mining algorithms with an evolutionary computation approach. More precisely, we propose a genetic p- gramming system (a type of evolutionary computation method that evolves c- puter programs) to automate the design of rule induction algorithms, a type of cl- si cation method that discovers a set of classi cation rules from data. We focus on genetic programming in this book because it is the paradigmatic type of machine learning method for automating the generation of programs and because it has the advantage of performing a global search in the space of candidate solutions (data mining algorithms in our case), but in principle other types of search methods for this task could be investigated in the future.

"Sobre este título" puede pertenecer a otra edición de este libro.

  • EditorialSpringer
  • Año de publicación2009
  • ISBN 10 3642025404
  • ISBN 13 9783642025402
  • EncuadernaciónTapa dura
  • IdiomaInglés
  • Número de páginas204
  • Contacto del fabricanteno disponible

Comprar usado

Condición: Excelente
Zustand: Sehr gut | Seiten: 204...
Ver este artículo

GRATIS gastos de envío desde Alemania a España

Destinos, gastos y plazos de envío

Comprar nuevo

Ver este artículo

EUR 19,49 gastos de envío desde Alemania a España

Destinos, gastos y plazos de envío

Otras ediciones populares con el mismo título

9783642261251: Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach (Natural Computing Series)

Edición Destacada

ISBN 10:  3642261256 ISBN 13:  9783642261251
Editorial: Springer, 2012
Tapa blanda

Resultados de la búsqueda para Automating the Design of Data Mining Algorithms: An...

Imagen de archivo

Alex Freitas, Gisele L. Pappa
Publicado por Springer Berlin Heidelberg, 2009
ISBN 10: 3642025404 ISBN 13: 9783642025402
Antiguo o usado Tapa dura

Librería: Buchpark, Trebbin, Alemania

Calificación del vendedor: 5 de 5 estrellas Valoración 5 estrellas, Más información sobre las valoraciones de los vendedores

Condición: Sehr gut. Zustand: Sehr gut | Seiten: 204 | Sprache: Englisch | Produktart: Bücher. Nº de ref. del artículo: 5923808/12

Contactar al vendedor

Comprar usado

EUR 66,88
Convertir moneda
Gastos de envío: GRATIS
De Alemania a España
Destinos, gastos y plazos de envío

Cantidad disponible: 1 disponibles

Añadir al carrito

Imagen del vendedor

Gisele L. Pappa|Alex Freitas
Publicado por Springer Berlin Heidelberg, 2009
ISBN 10: 3642025404 ISBN 13: 9783642025402
Nuevo Tapa dura
Impresión bajo demanda

Librería: moluna, Greven, Alemania

Calificación del vendedor: 5 de 5 estrellas Valoración 5 estrellas, Más información sobre las valoraciones de los vendedores

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book proposes a different goal for evolutionary algorithms in data mining: to automate the design of a data mining algorithm, rather than just optimize its parameters.Data mining is a very active research area with many successful real-world. Nº de ref. del artículo: 5043718

Contactar al vendedor

Comprar nuevo

EUR 92,27
Convertir moneda
Gastos de envío: EUR 19,49
De Alemania a España
Destinos, gastos y plazos de envío

Cantidad disponible: Más de 20 disponibles

Añadir al carrito

Imagen del vendedor

Alex Freitas
ISBN 10: 3642025404 ISBN 13: 9783642025402
Nuevo Tapa dura
Impresión bajo demanda

Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania

Calificación del vendedor: 5 de 5 estrellas Valoración 5 estrellas, Más información sobre las valoraciones de los vendedores

Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Data mining is a very active research area with many successful real-world app- cations. It consists of a set of concepts and methods used to extract interesting or useful knowledge (or patterns) from real-world datasets, providing valuable support for decision making in industry, business, government, and science. Although there are already many types of data mining algorithms available in the literature, it is still dif cult for users to choose the best possible data mining algorithm for their particular data mining problem. In addition, data mining al- rithms have been manually designed; therefore they incorporate human biases and preferences. This book proposes a new approach to the design of data mining algorithms. - stead of relying on the slow and ad hoc process of manual algorithm design, this book proposes systematically automating the design of data mining algorithms with an evolutionary computation approach. More precisely, we propose a genetic p- gramming system (a type of evolutionary computation method that evolves c- puter programs) to automate the design of rule induction algorithms, a type of cl- si cation method that discovers a set of classi cation rules from data. We focus on genetic programming in this book because it is the paradigmatic type of machine learning method for automating the generation of programs and because it has the advantage of performing a global search in the space of candidate solutions (data mining algorithms in our case), but in principle other types of search methods for this task could be investigated in the future. 204 pp. Englisch. Nº de ref. del artículo: 9783642025402

Contactar al vendedor

Comprar nuevo

EUR 106,99
Convertir moneda
Gastos de envío: EUR 11,00
De Alemania a España
Destinos, gastos y plazos de envío

Cantidad disponible: 2 disponibles

Añadir al carrito

Imagen del vendedor

Alex Freitas
Publicado por Springer Berlin Heidelberg, 2009
ISBN 10: 3642025404 ISBN 13: 9783642025402
Nuevo Tapa dura

Librería: AHA-BUCH GmbH, Einbeck, Alemania

Calificación del vendedor: 5 de 5 estrellas Valoración 5 estrellas, Más información sobre las valoraciones de los vendedores

Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Data mining is a very active research area with many successful real-world app- cations. It consists of a set of concepts and methods used to extract interesting or useful knowledge (or patterns) from real-world datasets, providing valuable support for decision making in industry, business, government, and science. Although there are already many types of data mining algorithms available in the literature, it is still dif cult for users to choose the best possible data mining algorithm for their particular data mining problem. In addition, data mining al- rithms have been manually designed; therefore they incorporate human biases and preferences. This book proposes a new approach to the design of data mining algorithms. - stead of relying on the slow and ad hoc process of manual algorithm design, this book proposes systematically automating the design of data mining algorithms with an evolutionary computation approach. More precisely, we propose a genetic p- gramming system (a type of evolutionary computation method that evolves c- puter programs) to automate the design of rule induction algorithms, a type of cl- si cation method that discovers a set of classi cation rules from data. We focus on genetic programming in this book because it is the paradigmatic type of machine learning method for automating the generation of programs and because it has the advantage of performing a global search in the space of candidate solutions (data mining algorithms in our case), but in principle other types of search methods for this task could be investigated in the future. Nº de ref. del artículo: 9783642025402

Contactar al vendedor

Comprar nuevo

EUR 106,99
Convertir moneda
Gastos de envío: EUR 11,99
De Alemania a España
Destinos, gastos y plazos de envío

Cantidad disponible: 1 disponibles

Añadir al carrito

Imagen de archivo

Pappa, Gisele L.; Freitas, Alex
Publicado por Springer, 2009
ISBN 10: 3642025404 ISBN 13: 9783642025402
Nuevo Tapa dura

Librería: Ria Christie Collections, Uxbridge, Reino Unido

Calificación del vendedor: 5 de 5 estrellas Valoración 5 estrellas, Más información sobre las valoraciones de los vendedores

Condición: New. In. Nº de ref. del artículo: ria9783642025402_new

Contactar al vendedor

Comprar nuevo

EUR 119,28
Convertir moneda
Gastos de envío: EUR 4,72
De Reino Unido a España
Destinos, gastos y plazos de envío

Cantidad disponible: Más de 20 disponibles

Añadir al carrito

Imagen del vendedor

Alex Freitas
ISBN 10: 3642025404 ISBN 13: 9783642025402
Nuevo Tapa dura

Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania

Calificación del vendedor: 5 de 5 estrellas Valoración 5 estrellas, Más información sobre las valoraciones de los vendedores

Buch. Condición: Neu. Neuware -Data mining is a very active research area with many successful real-world app- cations. It consists of a set of concepts and methods used to extract interesting or useful knowledge (or patterns) from real-world datasets, providing valuable support for decision making in industry, business, government, and science. Although there are already many types of data mining algorithms available in the literature, it is still dif cult for users to choose the best possible data mining algorithm for their particular data mining problem. In addition, data mining al- rithms have been manually designed; therefore they incorporate human biases and preferences. This book proposes a new approach to the design of data mining algorithms. - stead of relying on the slow and ad hoc process of manual algorithm design, this book proposes systematically automating the design of data mining algorithms with an evolutionary computation approach. More precisely, we propose a genetic p- gramming system (a type of evolutionary computation method that evolves c- puter programs) to automate the design of rule induction algorithms, a type of cl- si cation method that discovers a set of classi cation rules from data. We focus on genetic programming in this book because it is the paradigmatic type of machine learning method for automating the generation of programs and because it has the advantage of performing a global search in the space of candidate solutions (data mining algorithms in our case), but in principle other types of search methods for this task could be investigated in the future.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 204 pp. Englisch. Nº de ref. del artículo: 9783642025402

Contactar al vendedor

Comprar nuevo

EUR 106,99
Convertir moneda
Gastos de envío: EUR 35,00
De Alemania a España
Destinos, gastos y plazos de envío

Cantidad disponible: 2 disponibles

Añadir al carrito

Imagen de archivo

Pappa, Gisele L./ Freitas, Alex
Publicado por Springer-Verlag New York Inc, 2009
ISBN 10: 3642025404 ISBN 13: 9783642025402
Nuevo Tapa dura

Librería: Revaluation Books, Exeter, Reino Unido

Calificación del vendedor: 5 de 5 estrellas Valoración 5 estrellas, Más información sobre las valoraciones de los vendedores

Hardcover. Condición: Brand New. 1st edition. 200 pages. 9.45x6.38x0.71 inches. In Stock. Nº de ref. del artículo: x-3642025404

Contactar al vendedor

Comprar nuevo

EUR 155,22
Convertir moneda
Gastos de envío: EUR 11,86
De Reino Unido a España
Destinos, gastos y plazos de envío

Cantidad disponible: 2 disponibles

Añadir al carrito

Imagen de archivo

Pappa, Gisele L.; Freitas, Alex
Publicado por Springer, 2009
ISBN 10: 3642025404 ISBN 13: 9783642025402
Nuevo Tapa dura

Librería: Lucky's Textbooks, Dallas, TX, Estados Unidos de America

Calificación del vendedor: 5 de 5 estrellas Valoración 5 estrellas, Más información sobre las valoraciones de los vendedores

Condición: New. Nº de ref. del artículo: ABLIING23Mar3113020214147

Contactar al vendedor

Comprar nuevo

EUR 104,96
Convertir moneda
Gastos de envío: EUR 65,64
De Estados Unidos de America a España
Destinos, gastos y plazos de envío

Cantidad disponible: Más de 20 disponibles

Añadir al carrito