Introduction to Machine Learning and Bioinformatics
1st Edition
Published on 5. June 2008
384 pages
978-1-4200-1178-4 (ISBN)
Description
Lucidly Integrates Current Activities
Focusing on both fundamentals and recent advances, Introduction to Machine Learning and Bioinformatics presents an informative and accessible account of the ways in which these two increasingly intertwined areas relate to each other.
Examines Connections between Machine Learning & Bio
Focusing on both fundamentals and recent advances, Introduction to Machine Learning and Bioinformatics presents an informative and accessible account of the ways in which these two increasingly intertwined areas relate to each other.
Examines Connections between Machine Learning & Bio
More details
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Advanced undergraduate, graduate, and Ph.D. students in statistics, biostatistics, and computer science; professional statisticians, biostatisticians, computer scientists, and information scientists.
Illustrations
62 s/w Abbildungen, 19 s/w Tabellen, 1 s/w Photographie bzw. Rasterbild
62 b/w images, 19 tables and 1 halftone
ISBN-13
978-1-4200-1178-4 (9781420011784)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Sushmita Mitra | Sujay Datta | Theodore Perkins
Introduction to Machine Learning and Bioinformatics
Book
09/2019
1st Edition
Chapman & Hall/CRC
€96.70
Shipment within 15-20 days
Sushmita Mitra | Sujay Datta | Theodore Perkins
Introduction to Machine Learning and Bioinformatics
Book
06/2008
1st Edition
Chapman & Hall/CRC
€207.40
Shipment within 15-20 days
Persons
Mitra, Sushmita; Datta, Sujay; Perkins, Theodore; Michailidis, George
Content
Introduction. The Biology of a Living Organism. Probabilistic and Model-Based Learning. Classification Techniques. Unsupervised Learning Techniques. Computational Intelligence in Bioinformatics. Connections. Machine Learning in Structural Biology. Soft Computing in Biclustering. Bayesian Methods for Tumor Classification. Modeling and Analysis of iTRAQ Data. Mass Spectrometry Classification. Index.
