Scalable Pattern Recognition Algorithms - libro nuevo

Libro en la base de datos desde 2015-01-21T16:19:44+01:00 (Madrid)
Página de detalles modificada por última vez el 2023-12-21T10:17:39+01:00 (Madrid)
ISBN/EAN: 9783319056302

ISBN - escritura alterna:
3-319-05630-1, 978-3-319-05630-2
Mode alterno de escritura y términos de búsqueda relacionados:
Título del libro: pattern recognition, algorithms

Datos del la editorial

Autor: Pradipta Maji; Sushmita Paul
Título: Scalable Pattern Recognition Algorithms - Applications in Computational Biology and Bioinformatics
Editorial: Springer; Springer International Publishing
304 Páginas
Año de publicación: 2014-03-19
Cham; CH
Idioma: Inglés
96,29 € (DE)
99,00 € (AT)
118,00 CHF (CH)
Available
XXII, 304 p. 55 illus., 10 illus. in color.

EA; E107; eBook; Nonbooks, PBS / Biologie/Sonstiges; DV-gestützte Biologie/Bioinformatik; Verstehen; Artificial Intelligence; Bioinformatics; Computational Biology; Computational Intelligence; Data Mining; Machine Learning; Medical Imaging; Pattern Recognition; Soft Computing; B; Computational and Systems Biology; Automated Pattern Recognition; Artificial Intelligence; Data Mining and Knowledge Discovery; Radiology; Computer Science; Mustererkennung; Künstliche Intelligenz; Data Mining; Wissensbasierte Systeme, Expertensysteme; Bildgebende Verfahren; BB

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From the book reviews:

“This book provides unique insights into how various soft computing and machine learning methods can be formulated and used in building efficient pattern recognition models. … This is a great resource to students and researchers in the fields of computer science, electrical and biomedical engineering. The author has explained the complex ideas through numerous examples which make conceptualization easy. … The well-organized chapters as well as use of different notations and typescripts make it a user-friendly reference.” (Parthiv Amin, Doody’s Book Reviews, August, 2014)

is a Research Associate at the same institution.

Recent advances in high-throughput technologies have resulted in a deluge of biological information. Yet the storage, analysis, and interpretation of such multifaceted data require effective and efficient computational tools.

This unique text/reference addresses the need for a unified framework describing how soft computing and machine learning techniques can be judiciously formulated and used in building efficient pattern recognition models. The book reviews both established and cutting-edge research, following a clear structure reflecting the major phases of a pattern recognition system: classification, feature selection, and clustering. The text provides a careful balance of theory, algorithms, and applications, with a particular emphasis given to applications in computational biology and bioinformatics.

This important work will be of great use to graduate students and researchers in the fields of computer science, electrical and biomedical engineering. Researchers and practitioners involved in pattern recognition, machine learning, computational biology and bioinformatics, data mining, and soft computing will also find the book invaluable.