Machine learning and statistics : the interface
Author(s)
Bibliographic Information
Machine learning and statistics : the interface
（Sixth-generation computer technology series）
Wiley, c1997
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Note
Papers from a workshop which followed the European Conference in Machine Learning (ECML-94)
"A Wiley-Interscience publication."f
Description and Table of Contents
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.
Table of Contents
- 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.
by "Nielsen BookData"