Invariant Image Recognition by Zernike Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparison of a neural network and a piecewise linear classifier
Pattern Recognition Letters
On Piecewise-Linear Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Neural Modeling of Piecewise Linear Classifiers
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A Recursive Partitioning Decision Rule for Nonparametric Classification
IEEE Transactions on Computers
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Finding the Extrema of a Region
IEEE Transactions on Pattern Analysis and Machine Intelligence
Locally Trained Piecewise Linear Classifiers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental local linear fuzzy classifier in fisher space
EURASIP Journal on Advances in Signal Processing
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A simple and fast multi-class piecewise linear classifier is proposed and implemented. For a pair of classes, the piecewise linear boundary is a collection of segments of hyperplanes created as perpendicular bisectors of line segments linking centroids of the classes or parts of classes. For a multi-class problem, a binary partition tree is initially created which represents a hierarchical division of given pattern classes into groups, with each non-leaf node corresponding to some group. After that, a piecewise linear boundary is constructed for each non-leaf node of the partition tree as for a two-class problem. The resulting piecewise linear boundary is a set of boundaries corresponding to all non-leaf nodes of the tree. The basic data structures of algorithms of synthesis of a piecewise linear classifier and classification of unknown patterns are described. The proposed classifier is compared with a number of known pattern classifiers by benchmarking with the use of real-world data sets.