Artículos relacionados a Genetic Programming for Image Classification: An Automated...

Genetic Programming for Image Classification: An Automated Approach to Feature Learning: 24 (Adaptation, Learning, and Optimization) - Tapa blanda

 
9783030659295: Genetic Programming for Image Classification: An Automated Approach to Feature Learning: 24 (Adaptation, Learning, and Optimization)

Sinopsis

This book offers several new GP approaches to feature learning for image classification. Image classification is an important task in computer vision and machine learning with a wide range of applications. Feature learning is a fundamental step in image classification, but it is difficult due to the high variations of images. Genetic Programming (GP) is an evolutionary computation technique that can automatically evolve computer programs to solve any given problem. This is an important research field of GP and image classification. No book has been published in this field. This book shows how different techniques, e.g., image operators, ensembles, and surrogate, are proposed and employed to improve the accuracy and/or computational efficiency of GP for image classification. The proposed methods are applied to many different image classification tasks, and the effectiveness and interpretability of the learned models will be demonstrated. This book is suitable as a graduate andpostgraduate level textbook in artificial intelligence, machine learning, computer vision, and evolutionary computation.   

 


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

Acerca del autor

Charlotte y Peter Fiell son dos autoridades en historia, teoría y crítica del diseño y han escrito más de sesenta libros sobre la materia, muchos de los cuales se han convertido en éxitos de ventas. También han impartido conferencias y cursos como profesores invitados, han comisariado exposiciones y asesorado a fabricantes, museos, salas de subastas y grandes coleccionistas privados de todo el mundo. Los Fiell han escrito numerosos libros para TASCHEN, entre los que se incluyen 1000 Chairs, Diseño del siglo XX, El diseño industrial de la A a la Z, Scandinavian Design y Diseño del siglo XXI.

De la contraportada

<p>This book offers several new GP approaches to feature learning for image classification. Image classification is an important task in computer vision and machine learning with a wide range of applications. Feature learning is a fundamental step in image classification, but it is difficult due to the high variations of images. Genetic Programming (GP) is an evolutionary computation technique that can automatically evolve computer programs to solve any given problem. This is an important research field of GP and image classification. No book has been published in this field. This book shows how different techniques, e.g., image operators, ensembles, and surrogate, are proposed and employed to improve the accuracy and/or computational efficiency of GP for image classification. The proposed methods are applied to many different image classification tasks, and the effectiveness and interpretability of the learned models will be demonstrated. This book is suitable as a graduate and postgraduate level textbook in artificial intelligence, machine learning, computer vision, and evolutionary computation.&nbsp; &nbsp;</p><p>&nbsp;</p><br>

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

  • EditorialSpringer
  • Año de publicación2022
  • ISBN 10 3030659291
  • ISBN 13 9783030659295
  • EncuadernaciónTapa blanda
  • IdiomaInglés
  • Número de edición1
  • Número de páginas288
  • Contacto del fabricanteno disponible

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

9783030659264: Genetic Programming for Image Classification: An Automated Approach to Feature Learning: 24 (Adaptation, Learning, and Optimization)

Edición Destacada

ISBN 10:  3030659267 ISBN 13:  9783030659264
Editorial: Springer, 2021
Tapa dura

Resultados de la búsqueda para Genetic Programming for Image Classification: An Automated...

Imagen del vendedor

Ying Bi|Bing Xue|Mengjie Zhang
Publicado por Springer International Publishing, 2022
ISBN 10: 3030659291 ISBN 13: 9783030659295
Nuevo Tapa blanda
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. Introduces a series of typical Genetic Programming-based approaches to feature learning in image classificationProvides broad perceptive insights on what and how Genetic Programming can offer and shows a comprehensive and systematic research route. Nº de ref. del artículo: 564797287

Contactar al vendedor

Comprar nuevo

EUR 136,16
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

Ying Bi
ISBN 10: 3030659291 ISBN 13: 9783030659295
Nuevo Taschenbuch
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

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book offers several new GP approaches to feature learning for image classification. Image classification is an important task in computer vision and machine learning with a wide range of applications. Feature learning is a fundamental step in image classification, but it is difficult due to the high variations of images. Genetic Programming (GP) is an evolutionary computation technique that can automatically evolve computer programs to solve any given problem. This is an important research field of GP and image classification. No book has been published in this field. This book shows how different techniques, e.g., image operators, ensembles, and surrogate, are proposed and employed to improve the accuracy and/or computational efficiency of GP for image classification. The proposed methods are applied to many different image classification tasks, and the effectiveness and interpretability of the learned models will be demonstrated. This book is suitable as a graduate and postgraduate level textbook in artificial intelligence, machine learning, computer vision, and evolutionary computation. 288 pp. Englisch. Nº de ref. del artículo: 9783030659295

Contactar al vendedor

Comprar nuevo

EUR 160,49
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

Ying Bi
Publicado por Springer International Publishing, 2022
ISBN 10: 3030659291 ISBN 13: 9783030659295
Nuevo Taschenbuch

Librería: AHA-BUCH GmbH, Einbeck, Alemania

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

Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book offers several new GP approaches to feature learning for image classification. Image classification is an important task in computer vision and machine learning with a wide range of applications. Feature learning is a fundamental step in image classification, but it is difficult due to the high variations of images. Genetic Programming (GP) is an evolutionary computation technique that can automatically evolve computer programs to solve any given problem. This is an important research field of GP and image classification. No book has been published in this field. This book shows how different techniques, e.g., image operators, ensembles, and surrogate, are proposed and employed to improve the accuracy and/or computational efficiency of GP for image classification. The proposed methods are applied to many different image classification tasks, and the effectiveness and interpretability of the learned models will be demonstrated. This book is suitable as a graduate andpostgraduate level textbook in artificial intelligence, machine learning, computer vision, and evolutionary computation. Nº de ref. del artículo: 9783030659295

Contactar al vendedor

Comprar nuevo

EUR 160,49
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

Bi, Ying; Xue, Bing; Zhang, Mengjie
Publicado por Springer, 2022
ISBN 10: 3030659291 ISBN 13: 9783030659295
Nuevo Tapa blanda

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: ria9783030659295_new

Contactar al vendedor

Comprar nuevo

EUR 170,18
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

Ying Bi
ISBN 10: 3030659291 ISBN 13: 9783030659295
Nuevo Taschenbuch

Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania

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

Taschenbuch. Condición: Neu. Neuware -This book offers several new GP approaches to feature learning for image classification. Image classification is an important task in computer vision and machine learning with a wide range of applications. Feature learning is a fundamental step in image classification, but it is difficult due to the high variations of images. Genetic Programming (GP) is an evolutionary computation technique that can automatically evolve computer programs to solve any given problem. This is an important research field of GP and image classification. No book has been published in this field. This book shows how different techniques, e.g., image operators, ensembles, and surrogate, are proposed and employed to improve the accuracy and/or computational efficiency of GP for image classification. The proposed methods are applied to many different image classification tasks, and the effectiveness and interpretability of the learned models will be demonstrated. This book is suitable as a graduate andpostgraduate level textbook in artificial intelligence, machine learning, computer vision, and evolutionary computation.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 288 pp. Englisch. Nº de ref. del artículo: 9783030659295

Contactar al vendedor

Comprar nuevo

EUR 160,49
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

Bi, Ying; Xue, Bing; Zhang, Mengjie
Publicado por Springer, 2022
ISBN 10: 3030659291 ISBN 13: 9783030659295
Nuevo Tapa blanda

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: ABLIING23Mar3113020025742

Contactar al vendedor

Comprar nuevo

EUR 161,19
Convertir moneda
Gastos de envío: EUR 65,75
De Estados Unidos de America a España
Destinos, gastos y plazos de envío

Cantidad disponible: Más de 20 disponibles

Añadir al carrito