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The margitron: a generalized perceptron with margin.

Constantinos Panagiotakopoulos¹, Petroula Tsampouka

¹Physics Division, School of Technology, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece. costapan@eng.auth.gr

IEEE Transactions on Neural Networks

|January 11, 2011

Summary

This summary is machine-generated.

The margitron, a large margin classifier, converges efficiently in incremental settings. It achieves competitive results against other methods on hard margin tasks.

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Area of Science:

Machine Learning
Computational Theory

Background:

The perceptron algorithm is a foundational model in machine learning.
Large margin classifiers aim to find decision boundaries with maximal separation.
Existing methods like Support Vector Machines (SVMs) and gradient descent are widely used.

Purpose of the Study:

To introduce and analyze the margitron, a novel family of large margin classifiers.
To demonstrate the margitron's convergence properties and its ability to achieve desired margin fractions.
To compare the margitron's performance against established classification algorithms.

Main Methods:

Identifying the classical perceptron algorithm with margin as part of the margitron family.
Analyzing the margitron's convergence in an incremental setting.
Conducting comparative experiments using linear kernels on hard margin tasks.

Main Results:

The margitron converges in a finite number of updates to solutions with a controllable fraction of the maximum margin.
Experimental results show the margitron is highly competitive with decomposition SVMs, cutting-plane algorithms, and gradient descent methods.
The margitron offers a competitive alternative for hard margin classification tasks.

Conclusions:

The margitron represents a significant advancement in large margin classification.
Its efficient convergence and competitive performance make it a valuable tool for machine learning practitioners.
Further research into the margitron family could yield additional benefits for classification problems.