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Know Inf SST (2006) 10(4): 453 472 DOI 10.1007/s10115-006-0013-y Knowledge and Information Systems R E G U L A R PA P E R Tao Li Shanghai Zhu Mitsuki O'Hara Using discriminant analysis for multi-class

To deal with this issue we introduce discriminant analysis for multi-class classification, in which the classification and comparison data are classified into classes and the classification error (satellite) data are classified as the respective class. Our approach is multi-class, but discriminant analysis is usually performed in one class, which makes it hard to reason about the class structure of the training data. We present both supervised and unsupervised classification results, in which we find that the discriminant analysis is more appropriate than cross-validation for multi-class classification tasks. Keywords: Knowledge and Information Systems, Classifying data, Multi-class classification, Multi-class classification error, Information theory. Subject(s): Information Theory, Statistics, Statistical analysis, Machine Learning, Statistics. Keywords: Knowledge and Information Systems, Classifying data, Multi-class classification, Multi-class classification error, Information theory. Received: 4 October 2004 / Revised: 13 June 2005 / Accepted: 25 June 2005 / Published online: 24 March 2006 C Springer-Verlag London Limited Search for similar articles in Coppers: Article ID: 10.2490/.34.4.485 Published: 08 July 2006 Date: 8 July 2006 Article Title: Multistate multigrid decision problem: a Bayesian approach. Author(s): Nadella Estemirova, Mikhail V. Kuznets ova, Alexey A. Krivoshvili. Department of Computer Science, University of Maryland, College Park, MD 20621, USA. Abstract Multistate multigrid decision problem (MODEM), an integral part of the Internet architecture, provides a unique dataset for researchers in decision science, computer assisted design, and the field of information theory. In the past we have solved the task of selecting a candidate ISP for the Internet traffic control system. Our main result is the identification of the best solution from among the many solutions, thus providing important information for the design development of the Internet infrastructure. However, MODEM is the first data that is available in such a large scale for the identification of candidate operators. The primary purpose of this study is to find an efficient method of the clustering of MODEM, which enables the identification of the good performing operators with more accuracy. We find that both hierarchical clustering algorithms K-means and radial basis functions can be applied to the problem.

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Discriminant analysis is a statistical technique used to determine which variables are most effective in predicting the category or group to which a case or observation belongs.

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There is no specific requirement for individuals or entities to file using discriminant analysis. It is a statistical technique used by researchers, analysts, or data scientists to analyze data and make predictions.

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To fill out using discriminant analysis, you need to gather the necessary data, identify the predictor variables, choose the appropriate discriminant analysis method, perform the analysis using statistical software, and interpret the results.

What is the purpose of using discriminant analysis for?

The purpose of using discriminant analysis is to understand the relationship between predictor variables and categorical outcomes or groups. It helps in classifying or predicting cases based on the given set of predictor variables.

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The information reported in a discriminant analysis includes the predictor variables, their weights or coefficients, the discriminant functions, group means, group centroids, classification results, and measures of model fit.

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