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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… 
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Related topics

Related topics

12 relations
Cross-validation (statistics)Deep learningEmpirical risk minimizationGradient descent

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2018
2018
Online Anomaly Detection With Minimax Optimal Density Estimation in Nonstationary Environments
We introduce a truly online anomaly detection algorithm that sequentially processes data to detect anomalies in time series. In… 
Highly Cited
2018
Highly Cited
2018
Convolutional neural network-based multimodal image fusion via similarity learning in the shearlet domain
Recently, deep learning has been shown effectiveness in multimodal image fusion. In this paper, we propose a fusion method for CT… 
2017
2017
On the problem of on-line learning with log-loss
In this paper we consider the problem of on-line learning with respect to the logarithmic loss, where the learner provides a… 
Highly Cited
2015
Highly Cited
2015
Online Stochastic Linear Optimization under One-bit Feedback
In this paper, we study a special bandit setting of online stochastic linear optimization, where only one-bit of information is… 
Highly Cited
2015
Highly Cited
2015
A Learning-Rate Schedule for Stochastic Gradient Methods to Matrix Factorization
Stochastic gradient methods are effective to solve matrix factorization problems. However, it is well known that the performance… 
2014
2014
Dropout Training for Support Vector Machines
Dropout and other feature noising schemes have shown promising results in controlling over-fitting by artificially corrupting… 
2012
2012
Probabilistic estimation while ignoring outliers
  • 2012
  • Corpus ID: 16523868
Logistic regression learns a parameterized mapping from feature vectors to probability vectors and is for example central to… 
2011
2011
A Primal-Dual Convergence Analysis of Boosting
Boosting combines weak learners into a predictor with low empirical risk. Its dual constructs a high entropy distribution upon… 
2011
2011
Efficiently Learning a Distance Metric for Large Margin Nearest Neighbor Classification
We concern the problem of learning a Mahalanobis distance metric for improving nearest neighbor classification. Our work is… 
Highly Cited
2008
Highly Cited
2008
A quasi-Newton approach to non-smooth convex optimization
We extend the well-known BFGS quasi-Newton method and its limited-memory variant LBFGS to the optimization of non-smooth convex… 
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