Loss functions for classification

Known as: Logistic loss

In machine learning and mathematical optimization, loss functions for classification are computationally feasible loss functions representing the…

Papers overview

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2019

The predictive quality of machine learning models is typically measured in terms of their (approximate) expected prediction…

2018

Nowadays, population is growing on large scale along with the problems faced by the people are also increasing. Thus, healthcare…

2018

The predictive quality of machine learning models is typically measured in terms of their (approximate) expected prediction error…

2018

Deep neural network (DNN) has been increasingly applied to travel demand prediction. However, no study has examined how DNN…

2017

Environment perception is a critical enabler for automated driving systems since it allows a comprehensive understanding of…

2016

Collaborative filtering (CF)-based methods in recommender systems believe that the user’s preference of an item is the…

2016

2012

In large-scale machine learning, available memory (RAM) is often a key constraint, both during model training and when making new…

2006

We provide a PAC-Bayesian bound for the expected loss of convex combinations of classifiers under a wide class of loss functions…

1999

In this paper we introduce a general method that allows to prove tight linear inequalities between different types of predictive…