Machine Learning and Statistics
The Interface
Published on 11. December 1996
Book
Hardback
364 pages
978-0-471-14890-6 (ISBN)
Description
This work examines the intersection of machine learning and statistics, an expanding area of interest to data analysis and intelligent systems students and professionals. Through a series of papers, the book shows how machine learning researchers are applying statistical and probabilistic approaches in the development of a variety of machine learning algorithms. It examines classification and prediction, opportunities and problems created by the expansion of database use around the world, and how some machine learning algorithms are currently used to perform classification and forecasting tasks which were previously in the domain of statisticians.
More details
Series
Language
English
Place of publication
New York
United States
Publishing group
John Wiley and Sons Ltd
Target group
College/higher education
Professional and scholarly
Illustrations
Illustrations
Dimensions
Height: 165 mm
Width: 240 mm
Weight
737 gr
ISBN-13
978-0-471-14890-6 (9780471148906)
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Schweitzer Classification
Content
Statistical Properties of Tree-Based Approaches to Classification; The Decision Tree Algorithm CAL5 Based on a Statistical Approach to its Splitting Algorithm; Probabilistic Symbolic Classifiers: An Empirical Comparison from a Statistical Perspective; A Multistrategy Approach to Learning Multiple Dependent Concepts; Quality of Decision Rules - Definition and Classification Schemes for Multiple Rules; DIPOL - A Hybrid Piecewise Linear Classifier; Combining Classification Procedures; Distance-based Decision Trees; Learning Fuzzy Controllers from Examples; Some Developments in Statistical Credit Scoring; Combination of Statistical and Other Learning Methods to Predict Financial Time Series.