Results 31 to 40 of about 701,150 (283)
Variable Selection and Parameter Tuning in High-Dimensional Prediction [PDF]
, 2010 In the context of classification using high-dimensional data such as microarray gene expression data, it is often useful to perform preliminary variable selection.
Bernau, Christoph +1 more
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Optimization of Neural Network-Based Self-Tuning PID Controllers for Second Order Mechanical Systems
Applied Sciences, 2021 The feasibility of a neural network method was discussed in terms of a self-tuning proportional–integral–derivative (PID) controller. The proposed method was configured with two neural networks to dramatically reduce the number of tuning attempts with a ...
Yong-Seok Lee, Dong-Won Jang
doaj +1 more source
Calibrating the GAMIL3-1° climate model using a derivative-free optimization method [PDF]
Geoscientific Model DevelopmentParameterization in climate models often involves parameters that are poorly constrained by observations or theoretical understanding alone. Manual tuning by experts can be time-consuming, subjective, and prone to underestimating uncertainties. Automated
W. Liang +10 more
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Optimizing Performance of Hadoop with Parameter Tuning
ITM Web of Conferences, 2017 Optimizing Hadoop with the parameter tuning is an effective way to greatly improve the performance, but it usually costs too much time to identify the optimal parameters configuration because there are many parameters. Users are always blindly adjust too
Chen Xiang +4 more
doaj +1 more source
A parameter-free learning automaton scheme
Frontiers in Neurorobotics, 2022 For a learning automaton, a proper configuration of the learning parameters is crucial. To ensure stable and reliable performance in stochastic environments, manual parameter tuning is necessary for existing LA schemes, but the tuning procedure is time ...
Xudie Ren, Shenghong Li, Hao Ge
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Towards Self-Tuning Parameter Servers [PDF]
2020 IEEE International Conference on Big Data (Big Data), 2020 Recent years, many applications have been driven advances by the use of Machine Learning (ML). Nowadays, it is common to see industrial-strength machine learning jobs that involve millions of model parameters, terabytes of training data, and weeks of training. Good efficiency, i.e., fast completion time of running a specific ML job, therefore, is a key
Liu, Chris +6 more
openaire +2 more sources
Parameter-tuning Networks: Experiments and Active Walk Model
, 2008 The tuning process of a large apparatus of many components could be represented and quantified by constructing parameter-tuning networks. The experimental tuning of the ion source of the neutral beam injector of HT-7 Tokamak is presented as an example ...
Han, Xiao-Pu +3 more
core +1 more source
Naturalness of Neutralino Dark Matter
, 2013 We investigate the level of fine-tuning of neutralino Dark Matter below 200 GeV in the low-energy phenomenological minimal supersymmetric Standard Model taking into account the newest results from XENON100 and the Large Hadron Collider as well as all ...
Grothaus, Philipp +2 more
core +1 more source
Robust Tuning Datasets for Statistical Machine Translation
, 2017 We explore the idea of automatically crafting a tuning dataset for Statistical Machine Translation (SMT) that makes the hyper-parameters of the SMT system more robust with respect to some specific deficiencies of the parameter tuning algorithms.
Nakov, Preslav, Vogel, Stephan
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Parameter Tuning Using Gaussian Processes [PDF]
, 2012 Most machine learning algorithms require us to set up their parameter values before applying these algorithms to solve problems. Appropriate parameter settings will bring good performance while inappropriate parameter settings generally result in poor ...
Ma, Jinjin
core +1 more source