Logo

Machine Learning, Neural and Statistical Classification

Large book cover: Machine Learning, Neural and Statistical Classification

Machine Learning, Neural and Statistical Classification
by

Publisher: Ellis Horwood
ISBN/ASIN: 013106360X
ISBN-13: 9780131063600
Number of pages: 298

Description:
The aim of this book is to provide an up-to-date review of different approaches to classification, compare their performance on a wide range of challenging data-sets, and draw conclusions on their applicability to realistic industrial problems. As the book's title suggests. a wide variety of approaches has been taken towards this task. Three main historical strands of research can be identified: statistical, machine learning and neural network.

Home page url

Download or read it online for free here:
Download link
(1.7MB, PDF)

Similar books

Book cover: Introduction To Machine LearningIntroduction To Machine Learning
by
This book concentrates on the important ideas in machine learning, to give the reader sufficient preparation to make the extensive literature on machine learning accessible. The author surveys the important topics in machine learning circa 1996.
(26722 views)
Book cover: Machine Learning for DesignersMachine Learning for Designers
by - O'Reilly Media
This book introduces you to contemporary machine learning systems and helps you integrate machine-learning capabilities into your user-facing designs. Patrick Hebron explains how machine-learning applications can affect the way you design websites.
(4972 views)
Book cover: Machine LearningMachine Learning
by - InTech
Neural machine learning approaches, Hamiltonian neural networks, similarity discriminant analysis, machine learning methods for spoken dialogue simulation and optimization, linear subspace learning for facial expression analysis, and more.
(14231 views)
Book cover: An Introduction to Statistical LearningAn Introduction to Statistical Learning
by - Springer
This book provides an introduction to statistical learning methods. It contains a number of R labs with detailed explanations on how to implement the various methods in real life settings and it is a valuable resource for a practicing data scientist.
(8086 views)