The primary goal of pattern recognition is supervised or unsupervised classification. Among
the various frameworks in which pattern recognition has been traditionally formulated, the …

Decision trees have proved to be valuable tools for the description, classification and
generalization of data. Work on constructing decision trees from data exists in multiple …

S is a powerful environment for the statistical and graphical analysis of data. It provides the
tools to implement many statistical ideas that have been made possible by the widespread …

Decision trees are attractive classifiers due to their high execution speed. But trees derived
with traditional methods often cannot be grown to arbitrary complexity for possible loss of …

Much of previous attention on decision trees focuses on the splitting criteria and optimization
of tree sizes. The dilemma between overfitting and achieving maximum accuracy is seldom …

A survey is presented of current methods for decision tree classifier (DTC) designs and the
various existing issues. After considering potential advantages of DTCs over single-state …

Pattern recognition presents one of the most significant challenges for scientists and
engineers, and many different approaches have been proposed. The aim of this book is to …

Statistical pattern recognition is a very active area of study andresearch, which has seen
many advances in recent years. New andemerging applications-such as data mining, web …

During the last two decades a considerable amount of effort has been devoted to the
analysis of the influence of both training and testing sample size on the design and …

In this paper a review of the techniques used to solve the automatic target recognition (ATR)
problem is given. Emphasis is placed on algorithmic and implementation approaches. ATR …