Machine Learning, Neural and Statistical Classification
by D. Michie, D. J. Spiegelhalter
Publisher: Ellis Horwood 1994
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.
Download or read it online for free here:
Download link
(1.7MB, PDF)
Similar books
Foundations of Machine Learningby M. Mohri, A. Rostamizadeh, A. Talwalkar - The MIT Press
This is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools.
(3095 views)
Understanding Machine Learning: From Theory to Algorithmsby Shai Shalev-Shwartz, Shai Ben-David - Cambridge University Press
This book introduces machine learning and the algorithmic paradigms it offers. It provides a theoretical account of the fundamentals underlying machine learning and mathematical derivations that transform these principles into practical algorithms.
(6377 views)
Boosting: Foundations and Algorithmsby Robert E. Schapire, Yoav Freund - The MIT Press
Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate 'rules of thumb'. A remarkably rich theory has evolved around boosting, with connections to a range of topics.
(3999 views)
Statistical Learning and Sequential Predictionby Alexander Rakhlin, Karthik Sridharan - University of Pennsylvania
This text focuses on theoretical aspects of Statistical Learning and Sequential Prediction. The minimax approach, which we emphasize throughout the course, offers a systematic way of comparing learning problems. We will discuss learning algorithms...
(4362 views)