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Neural Networks and Support Vector Machine in Classification and Prediction of Carbonates Electrofacies

https://doi.org/10.2139/ssrn.4981592
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Outlier Detection
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Support-Vector Networks
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Deep learning for lithological classification of carbonate rock micro-CT images
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Estimation of prediction error by using K-fold cross-validation
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Complexity of training ReLU neural network
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Electrofacies classification of deeply buried carbonate strata using machine learning methods: A case study on ordovician paleokarst reservoirs in Tarim Basin
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3D modeling of reservoir electrofacies using integration clustering and geostatistic method in central field of Persian Gulf
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Research on convolutional neural network based on improved Relu piecewise activation function
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Least Squares Support Vector Machine Classifiers
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How Significant is a Boxplot Outlier?
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Sedimentology and Stratigraphy
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A training algorithm for optimal margin classifiers
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Consistency of cross validation for comparing regression procedures
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SVM Parameter Optimization using Grid Search and Genetic Algorithm to Improve Classification Performance
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Well Log Cluster Analysis: An Innovative Tool for Unconventional Exploration
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The Contribution of Logging Data to Sedimentology and Stratigraphy
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Outliers detection and treatment: a review.
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Sistemas petrolíferos e modelos de acumulação de hidrocarbonetos na Bacia de Santos
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Geophysical Reservoir Characterization and Monitoring at Brazilian Pre-Salt Oil Fields
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Applying supervised machine learning model to classify electrofacies in a Brazilian Pre-salt wellbore
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Scikit-learn: Machine Learning in Python
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Improving neural networks by preventing co-adaptation of feature detectors
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Adam: A Method for Stochastic Optimization
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Efficient softmax approximation for GPUs
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Reachable Set Computation and Safety Verification for Neural Networks with ReLU Activations
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On the Convergence of Adam and Beyond
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Deep learning for lithological classification of carbonate rock micro-CT images
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A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
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No Unbiased Estimator of the Variance of K-Fold Cross-Validation
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