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Loss function

Known as: Zero-one loss, Risk function, 0-1 loss function 
In mathematical optimization, statistics, decision theory and machine learning, a loss function or cost function is a function that maps an event or… (More)
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Topic mentions per year

Topic mentions per year

1951-2018
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Related topics

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Assignment problemBayes factorBayesian networkBlock-matching algorithm
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Related mentions per year

Related mentions per year

1936-2019
19401960198020002020
Loss function
Mathematical optimization
Statistical classification
Optimal control
Evolutionary algorithm
Bayesian network

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Review
2016
Review
2016
Concrete Problems in AI Safety
Rapid progress in machine learning and artificial intelligence (AI) has brought increasing attention to the potential impacts of… (More)
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Highly Cited
2016
Highly Cited
2016
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
We consider image transformation problems, where an input image is transformed into an output image. Recent methods for such… (More)
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Highly Cited
2016
Highly Cited
2016
Person Re-identification by Multi-Channel Parts-Based CNN with Improved Triplet Loss Function
Person re-identification across cameras remains a very challenging problem, especially when there are no overlapping fields of… (More)
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Highly Cited
2005
Highly Cited
2005
Estimation with Quadratic Loss
It has long been customary to measure the adequacy of an estimator by the smallness of its mean squared error. The least squares… (More)
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Highly Cited
2004
Highly Cited
2004
Are Loss Functions All the Same?
In this letter, we investigate the impact of choosing different loss functions from the viewpoint of statistical learning theory… (More)
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Highly Cited
2000
Highly Cited
2000
Loss Function Based Evaluation of DSGE Models
  • Frank Schorfheide, Yongsung Chang
  • 2000
In this paper we propose a Bayesian econometric procedure for the evaluation and comparison of DSGE models. Unlike in many… (More)
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Highly Cited
1999
Highly Cited
1999
Greedy Function Approximation: A Gradient Boosting Machine
Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org… (More)
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Highly Cited
1998
Highly Cited
1998
Sequential Prediction of Individual Sequences Under General Loss Functions
We consider adaptive sequential prediction of arbitrary binary sequences when the performance is evaluated using a general loss… (More)
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Highly Cited
1996
Highly Cited
1996
Bias Plus Variance Decomposition for Zero-One Loss Functions
We present a bias variance decomposition of expected misclassi cation rate the most commonly used loss function in supervised… (More)
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Highly Cited
1981
Highly Cited
1981
Pattern Recognition with Fuzzy Objective Function Algorithms
 
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