„Learning Control“
Suchergebnisse
9.512 Treffer
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Retraction Note: Secure prediction and assessment of sports injuries using deep learning based convolutional neural network
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Correction: Deep Transfer Learning-Based Automated Diabetic Retinopathy Detection Using Retinal Fundus Images in Remote Areas
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Correction: Application of supervised machine learning methods in injection molding process for initial parameters setting: prediction of the cooling time parameter
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Correction: Learning sample-aware threshold for semi-supervised learning
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Publisher Correction: Automating turbulence modelling by multi-agent reinforcement learning
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Author Correction: Augmenting vascular disease diagnosis by vasculature-aware unsupervised learning
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Publisher Correction: Automating turbulence modelling by multi-agent reinforcement learning
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Correction to: Adaptive submodular inverse reinforcement learning for spatial search and map exploration
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Correction to: A neural meta-model for predicting winter wheat crop yield
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Correction: Adversarial concept drift detection under poisoning attacks for robust data stream mining
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Retraction Note: How Have the COVID-19 Pandemic and Market Sentiment Affected the FX Market? Evidence from Statistical Models and Deep Learning Algorithms
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Correction to: Learning to bid and rank together in recommendation systems
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Correction to: Multi-agent reinforcement learning for fast-timescale demand response of residential loads
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Correction: Machine learning approach for the prediction of mining-induced stress in underground mines to mitigate ground control disasters and accidents
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Erratum to: Privileged Learning Using Regularization in the Problem of Evaluating the Human Posture
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Correction: Material hermeneutics as cultural learning: from relations to processes of relations
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Correction: Hierarchical reinforcement learning for kinematic control tasks with parameterized action spaces
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Correction to: efficient generator of mathematical expressions for symbolic regression
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What it takes to control AI by design: human learning
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A Systemic Approach for Identifying Parameter Errors in Simulation Models of Power Systems Using Machine Learning