4 papers
Convex losses and their applications to SVM, SVR, and Shallow Neural Networks
Filippo Portera
We propose multiple new convex losses for SVM and Neural Networks, applied to binary classification tasks. While there are practical limitations in exploiting them with the dual SV…
Convex Loss Functions for Support Vector Machines (SVMs) and Neural Networks
Filippo Portera
We propose a new convex loss for Support Vector Machines, both for the binary classification and for the regression models. Therefore, we show the mathematical derivation of the du…
A Generalized Weighted Loss for SVC and MLP
Filippo Portera
Usually standard algorithms employ a loss where each error is the mere absolute difference between the true value and the prediction, in case of a regression task. In the present,…
A generalized quadratic loss for SVM and Deep Neural Networks
Filippo Portera
We consider some supervised binary classification tasks and a regression task, whereas SVM and Deep Learning, at present, exhibit the best generalization performances. We extend th…