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Loss function

Known as: Zero-one loss, Loss, Risk function 
In mathematical optimization, statistics, decision theory and machine learning, a loss function or cost function is a function that maps an event or… 
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Related topics

Related topics

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Adaptive filterBackpropagationBootstrapping (statistics)Constrained optimization
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Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2017
Highly Cited
2017
Loss Functions for Image Restoration With Neural Networks
Neural networks are becoming central in several areas of computer vision and image processing and different architectures have… 
Highly Cited
2017
Highly Cited
2017
Proximal Policy Optimization Algorithms
We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through… 
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… 
Highly Cited
2016
Highly Cited
2016
You Only Look Once: Unified, Real-Time Object Detection
We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection… 
Highly Cited
2004
Highly Cited
2004
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
Stereo matching is one of the most active research areas in computer vision. While a large number of algorithms for stereo… 
Highly Cited
1997
Highly Cited
1997
Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces
A new heuristic approach for minimizing possiblynonlinear and non-differentiable continuous spacefunctions is presented. By means… 
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… 
Highly Cited
1992
Highly Cited
1992
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… 
Highly Cited
1981
Highly Cited
1981
Pattern Recognition with Fuzzy Objective Function Algorithms
  • J. Bezdek
  • Advanced Applications in Pattern Recognition
  • 1981
  • Corpus ID: 30806637
New updated! The latest book from a very famous author finally comes out. Book of pattern recognition with fuzzy objective… 
Highly Cited
1973
Highly Cited
1973
A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters
Abstract Two fuzzy versions of the k-means optimal, least squared error partitioning problem are formulated for finite subsets X… 
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