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A smooth perceptron algorithm

Negar Soheili (nsoheili***at***andrew.cmu.edu)
Javier Pena (jfp***at***andrew.cmu.edu)

Abstract: The perceptron algorithm, introduced in the late fifties in the machine learning community, is a simple greedy algorithm for finding a solution to a finite set of linear inequalities. The algorithm's main advantages are its simplicity and noise tolerance. The algorithm's main disadvantage is its slow convergence rate. We propose a modified version of the perceptron algorithm that retains the algorithm's original simplicity but has a substantially improved convergence rate.

Keywords: perceptron algorithm, smoothing technique, condition number

Category 1: Linear, Cone and Semidefinite Programming (Linear Programming )

Category 2: Convex and Nonsmooth Optimization (Convex Optimization )

Category 3: Convex and Nonsmooth Optimization (Nonsmooth Optimization )

Citation: Working Paper, Carnegie Mellon University, September 2011.

Download: [PDF]

Entry Submitted: 09/16/2011
Entry Accepted: 09/16/2011
Entry Last Modified: 09/22/2011

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