The Experts below are selected from a list of 337353 Experts worldwide ranked by ideXlab platform
H. Tamura - One of the best experts on this subject based on the ideXlab platform.
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Midpoint-Validation Method for Support Vector Machine Classification
IEICE Transactions on Information and Systems, 2008Co-Authors: H. Tamura, Koichi TannoAbstract:In this paper, we propose a midpoint-Validation Method which improves the generalization of Support Vector Machine. The proposed Method creates midpoint data, as well as a turning adjustment parameter of Support Vector Machine using midpoint data and previous training data. We compare its performance with the original Support Vector Machine, Multilayer Perceptron, Radial Basis Function Neural Network and also tested our proposed Method on several benchmark problems. The results obtained from the simulation shows the effectiveness of the proposed Method.
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Midpoint Validation Method for Support Vector Machine with Margin Adjustment Technique
2008 3rd International Conference on Innovative Computing Information and Control, 2008Co-Authors: H. Tamura, Keita TannoAbstract:In this paper, we propose a midpoint-Validation Method and margin adjustment technique which improves the generalization of support vector machine. Margin adjustment technique enables the nearly effect as soft margin support vector machine by adjusting parameter. The midpoint-Validation Method creates midpoint data, as well as a turning adjustment parameter of support vector machine using midpoint data and previous training data. We compare its performance with the support vector machine, soft margin support vector machine, multilayer perceptron, radial basis function neural network and also tested our proposed Method on fifth benchmark problems. The results obtained from the simulation shows the effectiveness of the proposed Method.
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Midpoint-Validation Method of Neural Networks for Pattern Classification Problems
Second International Conference on Innovative Computing Informatio and Control (ICICIC 2007), 2007Co-Authors: H. Tamura, Keita TannoAbstract:In this paper, we propose a midpoint-Validation Method, which improves the generalization of neural networks. The problem associated with the former cross Validation Method is that efficiency is affected due to the separation of training data into two or more set. As for the proposed Method, it creates midpoint data from the known training data and calculates a set of criteria using the newly created midpoint data and the previous training data. The implementation is easy since there is no unnecessary processing involved in separating the data into two or more sets. The advantage of the proposed Method is that the Method becomes much more efficient compared to the former Method due to the numerical simulation used. We compare its performance with those of the support vector machine (abbr. SVM), multilayer perceptron (abbr. MLP), radial basis function (abbr. RBF) and the proposed Method was tested on several benchmark problems. The results obtained from the simulation carried out shows the effectiveness of the proposed Method.
Keita Tanno - One of the best experts on this subject based on the ideXlab platform.
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Midpoint Validation Method for Support Vector Machine with Margin Adjustment Technique
2008 3rd International Conference on Innovative Computing Information and Control, 2008Co-Authors: H. Tamura, Keita TannoAbstract:In this paper, we propose a midpoint-Validation Method and margin adjustment technique which improves the generalization of support vector machine. Margin adjustment technique enables the nearly effect as soft margin support vector machine by adjusting parameter. The midpoint-Validation Method creates midpoint data, as well as a turning adjustment parameter of support vector machine using midpoint data and previous training data. We compare its performance with the support vector machine, soft margin support vector machine, multilayer perceptron, radial basis function neural network and also tested our proposed Method on fifth benchmark problems. The results obtained from the simulation shows the effectiveness of the proposed Method.
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Midpoint-Validation Method of Neural Networks for Pattern Classification Problems
Second International Conference on Innovative Computing Informatio and Control (ICICIC 2007), 2007Co-Authors: H. Tamura, Keita TannoAbstract:In this paper, we propose a midpoint-Validation Method, which improves the generalization of neural networks. The problem associated with the former cross Validation Method is that efficiency is affected due to the separation of training data into two or more set. As for the proposed Method, it creates midpoint data from the known training data and calculates a set of criteria using the newly created midpoint data and the previous training data. The implementation is easy since there is no unnecessary processing involved in separating the data into two or more sets. The advantage of the proposed Method is that the Method becomes much more efficient compared to the former Method due to the numerical simulation used. We compare its performance with those of the support vector machine (abbr. SVM), multilayer perceptron (abbr. MLP), radial basis function (abbr. RBF) and the proposed Method was tested on several benchmark problems. The results obtained from the simulation carried out shows the effectiveness of the proposed Method.
Gang Zhang - One of the best experts on this subject based on the ideXlab platform.
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review of the feature selective Validation Method fsv part i theory
IEEE Transactions on Electromagnetic Compatibility, 2018Co-Authors: A P Duffy, Antonio Orlandi, Gang ZhangAbstract:It has been. As an automated Validation Method recommended by the IEEE Standard 1597.1/2, the FSV Method has gained broad attention in the practice of computational electromagnetics modeling and simulations. This paper reviews the motivation, evolution, enhancements, and criticisms of the Method over this time. The aims of this paper are to give a detailed contextualization of the development of the FSV Method itself and to discuss the open questions and possible strategies of the next generation of automated Validation Methods.
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Review of the Feature Selective Validation Method (FSV). Part I—Theory
IEEE Transactions on Electromagnetic Compatibility, 2018Co-Authors: Alistair Duffy, Antonio Orlandi, Gang ZhangAbstract:It has been. As an automated Validation Method recommended by the IEEE Standard 1597.1/2, the FSV Method has gained broad attention in the practice of computational electromagnetics modeling and simulations. This paper reviews the motivation, evolution, enhancements, and criticisms of the Method over this time. The aims of this paper are to give a detailed contextualization of the development of the FSV Method itself and to discuss the open questions and possible strategies of the next generation of automated Validation Methods.
Tapio Pahikkala - One of the best experts on this subject based on the ideXlab platform.
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The spatial leave-pair-out cross-Validation Method for reliable AUC estimation of spatial classifiers
Data Mining and Knowledge Discovery, 2019Co-Authors: Antti Airola, Jonne Pohjankukka, Johanna Torppa, Maarit Middleton, Vesa Nykänen, Jukka Heikkonen, Tapio PahikkalaAbstract:Machine learning based classification Methods are widely used in geoscience applications, including mineral prospectivity mapping. Typical characteristics of the data, such as small number of positive instances, imbalanced class distributions and lack of verified negative instances make ROC analysis and cross-Validation natural choices for classifier evaluation. However, recent literature has identified two sources of bias, that can affect reliability of area under ROC curve estimation via cross-Validation on spatial data. The pooling procedure performed by Methods such as leave-one-out can introduce a substantial negative bias to results. At the same time, spatial dependencies leading to spatial autocorrelation can result in overoptimistic results, if not corrected for. In this work, we introduce the spatial leave-pair-out cross-Validation Method, that corrects for both of these biases simultaneously. The Methodology is used to benchmark a number of classification Methods on mineral prospectivity mapping data from the Central Lapland greenstone belt. The evaluation highlights the dangers of obtaining misleading results on spatial data and demonstrates how these problems can be avoided. Further, the results show the advantages of simple linear models for this classification task.
W J Zaaiman - One of the best experts on this subject based on the ideXlab platform.
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maximum power based pv performance Validation Method application to single axis tracking and fixed tilt c si systems in the italian alpine region
IEEE Journal of Photovoltaics, 2012Co-Authors: A Colli, W J ZaaimanAbstract:This paper presents springtime monitoring results for different crystalline-silicon (c-Si) photovoltaic (PV) systems installed at the multitechnology ground-mounted PV test field at the Airport Bolzano Dolomiti (ABD) located in the Italian Alps. The system data are analyzed and discussed.The main purpose of this paper is to validate the performance evaluation through a Methodology based on the effective maximum power of the PV modules. This approach could be useful when dealing, as in the present case, with commercial monitoring systems. Three different silicon-based technologies are taken into consideration: polycrystalline silicon, high-efficiency monocrystalline silicon, and hybrid monocrystalline silicon that have been positioned both on a single-axis tracker and on fixed 30°-tilted supports. The systems are connected to different types of inverter, through which the power monitoring is performed. The assessment shows indicators, such as final yield and performance ratio, for both tracked and fixed-tilt systems. The PV systems are evaluated in relation to irradiance data registered by two identical c-Si reference devices positioned on the tracker and on the fixed supports. Results show that an average difference of ±14 W exists between the module's label and the actual peak power. This difference is in line with the power tolerance declared by manufacturers. The maximum-power-based PV performance Validation Method could initially highlight cases in which a faulty module hides in the system, having the potential for application in fault detection and reliability analysis, followed by more specific evaluations.
