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Loss function
Known as:
Zero-one loss
, Loss
, Risk function
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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
50 relations
Adaptive filter
Backpropagation
Bootstrapping (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
Hang Zhao
,
Orazio Gallo
,
I. Frosio
,
J. Kautz
IEEE Transactions on Computational Imaging
2017
Corpus ID: 5334482
Neural networks are becoming central in several areas of computer vision and image processing and different architectures have…
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Highly Cited
2017
Highly Cited
2017
Proximal Policy Optimization Algorithms
J. Schulman
,
F. Wolski
,
Prafulla Dhariwal
,
Alec Radford
,
Oleg Klimov
ArXiv
2017
Corpus ID: 28695052
We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through…
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Highly Cited
2016
Highly Cited
2016
Person Re-identification by Multi-Channel Parts-Based CNN with Improved Triplet Loss Function
De Cheng
,
Yihong Gong
,
Sanping Zhou
,
Jinjun Wang
,
N. Zheng
IEEE Conference on Computer Vision and Pattern…
2016
Corpus ID: 3332134
Person re-identification across cameras remains a very challenging problem, especially when there are no overlapping fields of…
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Highly Cited
2016
Highly Cited
2016
You Only Look Once: Unified, Real-Time Object Detection
Joseph Redmon
,
S. Divvala
,
Ross B. Girshick
,
Ali Farhadi
IEEE Conference on Computer Vision and Pattern…
2016
Corpus ID: 206594738
We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection…
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Highly Cited
2004
Highly Cited
2004
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
D. Scharstein
,
R. Szeliski
International Journal of Computer Vision
2004
Corpus ID: 207745496
Stereo matching is one of the most active research areas in computer vision. While a large number of algorithms for stereo…
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Highly Cited
1997
Highly Cited
1997
Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces
R. Storn
,
K. Price
J. Glob. Optim.
1997
Corpus ID: 5297867
A new heuristic approach for minimizing possiblynonlinear and non-differentiable continuous spacefunctions is presented. By means…
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Highly Cited
1996
Highly Cited
1996
Bias Plus Variance Decomposition for Zero-One Loss Functions
Ron Kohavi
,
D. Wolpert
ICML
1996
Corpus ID: 14229903
We present a bias variance decomposition of expected misclassi cation rate the most commonly used loss function in supervised…
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Highly Cited
1992
Highly Cited
1992
Estimation with Quadratic Loss
W. James
,
C. Stein
1992
Corpus ID: 17984683
It has long been customary to measure the adequacy of an estimator by the smallness of its mean squared error. The least squares…
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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…
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Highly Cited
1973
Highly Cited
1973
A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters
J. Dunn
1973
Corpus ID: 120919314
Abstract Two fuzzy versions of the k-means optimal, least squared error partitioning problem are formulated for finite subsets X…
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