Linear classifier, least-squares cost function, and outliers
This is an incremental improvement for basic machine learning applications dealing with outlier sensitivity.
The paper addresses the negative impact of outliers on linear classifiers using a least-squares cost function and demonstrates that a simple scaling technique can reduce outlier significance to improve the decision boundary, with numerical results provided.
A set of introductory notes on the subject of data classification using a linear classifier and least-squares cost function, and the negative effect of the presence of outliers on the decision boundary of the linear discriminant. We also show how a simple scaling could make the outlier less significant, thereby obtaining a much better decision boundary. We present some numerical results.