Linear Discriminant Analysis | Linear discriminant analysis, Statistics, Machine learning, Linear combination, Features (pattern recognition), Linear classifier, Dimension reduction, Statistical classification

Frederic P. Miller (u. a.)

ISBN 10: 6130617178 / ISBN 13: 9786130617172

Published by OmniScriptum, 2026

About this Item

Linear Discriminant Analysis | Linear discriminant analysis, Statistics, Machine learning, Linear combination, Features (pattern recognition), Linear classifier, Dimension reduction, Statistical classification | Frederic P. Miller (u. a.) | Taschenbuch | Englisch | 2026 | OmniScriptum | EAN 9786130617172 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.

Seller Inventory # 101322077

Bibliographic Details

• Title: Linear Discriminant Analysis | Linear discriminant analysis, Statistics, Machine learning, Linear combination, Features (pattern recognition), Linear classifier, Dimension reduction, Statistical classification
• Publisher: OmniScriptum
• Publication Date: 2026
• Language: English
• ISBN 10: 6130617178
• ISBN 13: 9786130617172
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 6D millimeters 220 millimeters width by 150 millimeters height by 6 millimeters depth
• Weight: 143 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Linear discriminant analysis and the related Fisher's linear discriminant are methods used in statistics and machine learning to find the linear combination of features which best separate two or more classes of objects or events. The resulting combination may be used as a linear classifier, or, more commonly, for dimensionality reduction before later classification.LDA is closely related to ANOVA and regression analysis, which also attempt to express one dependent variable as a linear combination of other features or measurements. In the other two methods however, the dependent variable is a numerical quantity, while for LDA it is a categorical variable.LDA is also closely related to principal component analysis and factor analysis in that both look for linear combinations of variables which best explain the data. LDA explicitly attempts to model the difference between the classes of data.