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
Introduction to Machine Learning
by Amnon Shashua - arXiv
Introduction to Machine learning covering Statistical Inference (Bayes, EM, ML/MaxEnt duality), algebraic and spectral methods (PCA, LDA, CCA, Clustering), and PAC learning (the Formal model, VC dimension, Double Sampling theorem).
(23177 views)
by Amnon Shashua - arXiv
Introduction to Machine learning covering Statistical Inference (Bayes, EM, ML/MaxEnt duality), algebraic and spectral methods (PCA, LDA, CCA, Clustering), and PAC learning (the Formal model, VC dimension, Double Sampling theorem).
(23177 views)
Machine Learning and Data Mining: Lecture Notes
by Aaron Hertzmann - University of Toronto
Contents: Introduction to Machine Learning; Linear Regression; Nonlinear Regression; Quadratics; Basic Probability Theory; Probability Density Functions; Estimation; Classification; Gradient Descent; Cross Validation; Bayesian Methods; and more.
(10594 views)
by Aaron Hertzmann - University of Toronto
Contents: Introduction to Machine Learning; Linear Regression; Nonlinear Regression; Quadratics; Basic Probability Theory; Probability Density Functions; Estimation; Classification; Gradient Descent; Cross Validation; Bayesian Methods; and more.
(10594 views)
Reinforcement Learning
by C. Weber, M. Elshaw, N. M. Mayer - InTech
This book describes and extends the scope of reinforcement learning. It also shows that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional controllers.
(22168 views)
by C. Weber, M. Elshaw, N. M. Mayer - InTech
This book describes and extends the scope of reinforcement learning. It also shows that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional controllers.
(22168 views)
Inductive Logic Programming: Theory and Methods
by Stephen Muggleton, Luc de Raedt - ScienceDirect
Inductive Logic Programming is a new discipline which investigates the inductive construction of first-order clausal theories from examples and background knowledge. The authors survey the most important theories and methods of this new field.
(36831 views)
by Stephen Muggleton, Luc de Raedt - ScienceDirect
Inductive Logic Programming is a new discipline which investigates the inductive construction of first-order clausal theories from examples and background knowledge. The authors survey the most important theories and methods of this new field.
(36831 views)