Large Margin Classification Using the Perceptron Algorithm Large Margin Classification Using the Perceptron Algorithm
Paper summary This extends perceptrons \cite{books/daglib/0066902} but uses what is known as the Hinge loss (aka SVM loss): $$J_i(w) = max(0,\gamma -y\_i f(x\_i))$$ Where $\gamma$ is the margin. $J_i(w)$ is the error given some weight $w$ parameters. $x_i$ and $y_i$ are a training example and correct label. $f(x_i)$ is the perceptron function we are trying learn the best weights for.

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