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
The piecewise linear classifier DIPOL92
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Polyhedral separability through successive LP
Journal of Optimization Theory and Applications
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A simple and fast multi-class piecewise linear pattern classifier
Pattern Recognition
Locally Trained Piecewise Linear Classifiers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Methods of decreasing the number of support vectors via k-mean clustering
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
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In this paper the problem of finding piecewise linear boundaries between sets is considered and is applied for solving supervised data classification problems. An algorithm for the computation of piecewise linear boundaries, consisting of two main steps, is proposed. In the first step sets are approximated by hyperboxes to find so-called ''indeterminate'' regions between sets. In the second step sets are separated inside these ''indeterminate'' regions by piecewise linear functions. These functions are computed incrementally starting with a linear function. Results of numerical experiments are reported. These results demonstrate that the new algorithm requires a reasonable training time and it produces consistently good test set accuracy on most data sets comparing with mainstream classifiers.