Results 1 to 10 of about 105,297 (262)
(Multiscale) Cross-Entropy Methods: A Review [PDF]
Entropy, 2019 Cross-entropy was introduced in 1996 to quantify the degree of asynchronism between two time series. In 2009, a multiscale cross-entropy measure was proposed to analyze the dynamical characteristics of the coupling behavior between two sequences on ...
Antoine Jamin, Anne Humeau-Heurtier
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Tri-Training Algorithm for Adaptive Nearest Neighbor Density Editing and Cross Entropy Evaluation [PDF]
Entropy, 2023 Tri-training expands the training set by adding pseudo-labels to unlabeled data, which effectively improves the generalization ability of the classifier, but it is easy to mislabel unlabeled data into training noise, which damages the learning efficiency
Jia Zhao +4 more
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Rényi Cross-Entropy Measures for Common Distributions and Processes with Memory [PDF]
Entropy, 2022 Two Rényi-type generalizations of the Shannon cross-entropy, the Rényi cross-entropy and the Natural Rényi cross-entropy, were recently used as loss functions for the improved design of deep learning generative adversarial networks.
Ferenc Cole Thierrin +2 more
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Cross Entropy in Deep Learning of Classifiers Is Unnecessary—ISBE Error Is All You Need [PDF]
EntropyIn deep learning of classifiers, the cost function usually takes the form of a combination of SoftMax and CrossEntropy functions. The SoftMax unit transforms the scores predicted by the model network into assessments of the degree (probabilities) of an ...
Władysław Skarbek
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Action Recognition with 3D Residual Attention and Cross Entropy [PDF]
EntropyThis study proposes a three-dimensional (3D) residual attention network (3DRFNet) for human activity recognition by learning spatiotemporal representations from motion pictures.
Yuhao Ouyang, Xiangqian Li
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Symmetric Neutrosophic Cross Entropy Based Fault Recognition of Turbine [PDF]
Neutrosophic Sets and Systems, 2022 This study introduces a novel fault recognition methodology for turbine faults through symmetric trigonometric fuzzy and neutrosophic cross entropy measures (FCEM and NCEM) consequently.
C. P. Gandhi
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Security detection of network intrusion: application of cluster analysis method [PDF]
Компьютерная оптика, 2020 In order to resist network malicious attacks, this paper briefly introduced the network intrusion detection model and K-means clustering analysis algorithm, improved them, and made a simulation analysis on two clustering analysis algorithms on MATLAB ...
W.H. Yang
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Interregional analysis using a bi-regional input-output matrix for Argentina
Investigaciones Regionales - Journal of Regional Research, 2022 This paper presents a regional case study using a Bi-Regional Input-Output (BRIO) matrix of Buenos Aires City (BAC) and the Rest of Argentina (ROA), constructed from the Argentinian Input-Output matrix.
Leonardo J. Mastronardi +2 more
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Cross-entropy clustering [PDF]
Pattern Recognition, 2014 We construct a cross-entropy clustering (CEC) theory which finds the optimal number of clusters by automatically removing groups which carry no information. Moreover, our theory gives simple and efficient criterion to verify cluster validity. Although CEC can be build on an arbitrary family of densities, in the most important case of Gaussian CEC: {\em
Spurek, Przemysław, Tabor, Jacek
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An Attention-Based Spatiotemporal GGNN for Next POI Recommendation
IEEE Access, 2022 The task of Point-of-Interest (POI) recommendation is to recommend the next interest locations for users. Gated Graph Neural Network (GGNN) has been proved to be effective on POI recommendation tasks.
Quan Li +3 more
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