Results 111 to 120 of about 1,862,731 (241)
A Hybrid Optimization Model for Transformer Fault Diagnosis Based on Gas Classification
DigitalDissolved gas analysis (DGA) provides valuable information for transformer condition monitoring, yet accurate multi-class fault identification remains challenging due to overlapping gas patterns and the sensitivity of classifier hyperparameters.
Junju Lai +6 more
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Efficient Hyperparameter Tuning with Dynamic Accuracy Derivative-Free Optimization
, 2022 Many machine learning solutions are framed as optimization problems which rely on good hyperparameters. Algorithms for tuning these hyperparameters usually assume access to exact solutions to the underlying learning problem, which is typically not ...
Roberts, Lindon, Ehrhardt, Matthias
core
Dynamic Priors in Bayesian Optimization for Hyperparameter Optimization
CoRRBayesian optimization (BO) is a widely used approach to hyperparameter optimization (HPO). However, most existing HPO methods only incorporate expert knowledge during initialization, limiting practitioners' ability to influence the optimization process as new insights emerge. This limits the applicability of BO in iterative machine learning development
Lukas Fehring +5 more
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Learning General Gaussian Kernel Hyperparameters for SVR
, 2013 International audienceWe propose a new method for general gaussian kernel hyperparameters optimization for support vector regression. The hyperparameters are constrained to lie on a differentiable manifold. The proposed optimization technique is based on
Snoussi, Hichem +7 more
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Scientific ReportsGiven that the decision tree C4.5 algorithm has outstanding performance in prediction accuracy on medical datasets and is highly interpretable, this paper carries out an optimization study on the selection of hyperparameters of the algorithm in order to ...
Yiyan Zhang, Yi Xin, Qin Li
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On Optimizing Hyperparameters for Quantum Neural Networks
2024 IEEE International Conference on Quantum Computing and Engineering (QCE)The increasing capabilities of Machine Learning (ML) models go hand in hand with an immense amount of data and computational power required for training. Therefore, training is usually outsourced into HPC facilities, where we have started to experience limits in scaling conventional HPC hardware, as theorized by Moore's law.
Sabrina Herbst +2 more
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Fuzzy hyperparameters update in a second order optimization
This research will present a hybrid approach to accelerate convergence in a second order optimization. An online finite difference approximation of the diagonal Hessian matrix will be introduced, along with fuzzy inferencing of several hyperparameters ...
Bensadok, Abdelaziz +1 more
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Beyond Manual Tuning of Hyperparameters
, 2015 The success of hand-crafted machine learning systems in many applications raises the question of making machine learning algorithms more autonomous, i.e., to reduce the requirement of expert input to a minimum. We discuss two strategies towards this goal:
Hutter, Frank +2 more
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, 1994 Course notes and advertisement for a Short Course given at the Department of Electromagnetic Theory and Engineering, Duisburg University, Duisburg, Germany. The course was delivered by John W. Bandler and S.H. Chen on October 4 and 5, 1994.
Bandler, John W. +2 more
core +1 more source