Pattern classification and scene donkeytime.org | Cluster Analysis | Statistical ClassificationUse of Contents Link to ReefBase. Projects Partners Donors. Resources Status Threats Management. Tsunami Impacts. Search for coral reef related articles, reports and other publications. Please specify your search criteria, and indicate how many results are to be displayed.
Richard O. Duda
Pattern Recognition Theory and Applications pp Cite as. Statistical pattern recognition is now a mature discipline which has been successfully applied in several application domains. The primary goal in statistical pattern recognition is classification, where a pattern vector is assigned to one of a finite number of classes and each class is characterized by a probability density function on the measured features. A pattern vector is viewed as a point in the multidimensional space defined by the features. Design of a recognition system based on this paradigm requires careful attention to the following issues: type of classifier single-stage vs. Current research emphasis in pattern recognition is on designing efficient algorithms, studying small sample properties of various estimators and decision rules, implementing the algorithms on novel computer architecture, and incorporating context and domain-specific knowledge in decision making. Unable to display preview.
Emerson is about communication and the freedom of expression. Constitution and, therefore, the essay is primarily of interest to those concerned with such constitutional affairs. The essays aim at the educated general reader. Although one might dislike the layout of the book, one must concede that it is a recommendable introduction to communication studies. Pattern Classification and Scene Analysis. Richard 0.
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Richard O. Duda 33 Estimated H-index: Estimated H-index: Request Full-text. View in Source. Provides a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition.