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Zhong, Xin; Wang, Bing‐Zhong; Hao-cai, Wang

doi:10.1023/a:1015079619009

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Weka Computations1

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Cited by 4 publications

(2 citation statements)

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“…In this study, many algorithms available in WEKA were initially trained by using WEKA's default settings. After analysis of preliminary results, the algorithms that turned out to perform at the best (LibSVM [24][25][26][27][28], MultiLayerPerceptron [29] and SMOregression [30,31]) were re-trained by more finely tuning the parameter values in order to maximize the algorithm performance.…”

Section: Weka Computationsmentioning

confidence: 99%

See 1 more Smart Citation

Development of QSAR models for predicting hepatocarcinogenic toxicity of chemicals

Massarelli¹,

Imbriani²,

Coi³

et al. 2009

European Journal of Medicinal Chemistry

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Section: Weka Computationsmentioning

confidence: 99%

“…Further investigations were performed by changing the values of parameters, which enabled tuning the process of model development. The MultiLayerPerceptron [29], SMOregression [30,31] and LibSVM [24][25][26][27][28] methods, that had shown to give better results, were exploited in a more sophisticated search.…”

Section: Tr Set Statistics Ts Set Statisticsmentioning

confidence: 99%

Development of QSAR models for predicting hepatocarcinogenic toxicity of chemicals

Massarelli¹,

Imbriani²,

Coi³

et al. 2009

European Journal of Medicinal Chemistry

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Teaching application of MATLAB fuzzy logic toolbox to modeling coplanar waveguides

Übeylı

2008

Comp Applic In Engineering

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Adaptive neuro-fuzzy inference systems (ANFIS) have recently gained attention as fast and flexible vehicles to microwave modeling, simulation, and optimization. This article presents an initiative to teach the concept of modeling coplanar waveguides (CPWs) in the fuzzy logic toolbox of MATLAB to the electrical engineering students. The approach was based on illustrative applications that highlight the performance of ANFIS in the computation of the parameters of the microwave components. Following a brief description of CPWs, applications of the ANFIS modeling to the CPWs were done by means of a series of MATLAB functions. The author suggests that the use of MATLAB exercises will assist the students in gaining a better understanding of modeling CPWs by the use of ANFIS.

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Multilayer perceptron neural networks to compute quasistatic parameters of asymmetric coplanar waveguides

Übeylı

Güler²

2004

Neurocomputing

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Untitled

Cited by 4 publications

References 9 publications

Development of QSAR models for predicting hepatocarcinogenic toxicity of chemicals

Development of QSAR models for predicting hepatocarcinogenic toxicity of chemicals

Teaching application of MATLAB fuzzy logic toolbox to modeling coplanar waveguides

Multilayer perceptron neural networks to compute quasistatic parameters of asymmetric coplanar waveguides

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