Results 61 to 70 of about 2,269,659 (185)
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets
, 2017 Bayesian optimization has become a successful tool for hyperparameter optimization of machine learning algorithms, such as support vector machines or deep neural networks.
Bartels, Simon +4 more
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ACO for continuous function optimization: a performance analysis [PDF]
, 2014 The performance of the meta-heuristic algorithms often depends on their parameter settings. Appropriate tuning of the underlying parameters can drastically improve the performance of a meta-heuristic. The Ant Colony Optimization (ACO), a population based
Abraham, Ajith +2 more
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Optimization of myocardial function.
Basic research in cardiology, 1994 Under normal conditions the cardiac output is designed to meet the metabolic needs of the organism. Thus, the demands imposed on the heart muscle can range from low values at rest to an order of magnitude greater values during exercise. The heart uses a number of strategies to meet the short- and long-term changes in demand.
Alpert, N. R. +3 more
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Optimizing Generic Functions [PDF]
, 2004 Generic functions are defined by induction on the structural representation of types. As a consequence, by defining just a single generic operation, one acquires this operation over any particular type. An instance on a specific type is generated by interpretation of the type’s structure.
Alimarine, A., Smetsers, J.E.W.
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Multi-Criteria Land use Function Optimization
Real Estate Management and Valuation, 2015 Space as a public good should be used in a way that is consistent with recognized social, cultural, aesthetic, economical and ecological values. The optimization of space is associated with its limitations, thus it should be subjected to rational ...
Biłozor Andrzej +2 more
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The Sizing and Optimization Language (SOL): A computer language to improve the user/optimizer interface [PDF]
The nonlinear mathematical programming method (formal optimization) has had many applications in engineering design. A figure illustrates the use of optimization techniques in the design process. The design process begins with the design problem, such as
Lucas, S. H., Scotti, S. J.
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MQHOA algorithm with energy level stabilizing process
Tongxin xuebao, 2016 An improved multi-scale quantum harmonic oscillator algorithm (MQHOA) with energy level stabilizing process was proposed analogizing to quantum harmonic oscillator's wave function.
Peng WANG, Yan HUANG
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Bayesian Functional Optimization
Proceedings of the AAAI Conference on Artificial Intelligence, 2018 Bayesian optimization (BayesOpt) is a derivative-free approach for sequentially optimizing stochastic black-box functions. Standard BayesOpt, which has shown many successes in machine learning applications, assumes a finite dimensional domain which often is a parametric space.
Vien, Ngo Anh +2 more
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EWT多重分解与若干新型元启发式算法优化的多层感知器月径流预测
Renmin ZhujiangTo improve the accuracy of monthly runoff time series prediction, enhance the performance of multi-layer perceptron (MLP), and compare and verify the optimization effects of four new metaheuristic algorithms on benchmarking functions and instance ...
CAI Liang, BAO Yanfei, CUI Dongwen
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M-elite coevolutionary kinetic-molecular theory optimization algorithm
Tongxin xuebao, 2015 M-elite coevolutionary kinetic-molecular theory optimization algorithm (MECKMTOA) was proposed.MECKMTOA uses M elites to avoid misleading,improves the convergence precision by learning and collaboration among the elites,employs a new wave operator to ...
Chao-dong FAN, Jing ZHANG, Ling-zhi YI
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