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Hasil Pencarian

Ditemukan 3 dokumen yang sesuai dengan query
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Rarasmaya Indraswari
Rbf kernel optimization method with particle swarm optimization on svm using the Nlyaia of input data's movement
" SVM (Support Vector Machine) with RBF (Radial Basis Function) kernel is a frequently used classification method because usually it provides accurate results. The focus of most SVMoptimization research is the optimization of the input data, whereas the parameter of the kernel function (RBF), the sigma, which is used in SVM also has the potential to improve the performance of SVM when optimized. In this research, we proposed a new method of RBF kernel optimization with ... "
Surabaya: Faculty of Information Technology, Department of Informa Institut Teknologi Sepuluh Nopember, 2017
AJ-Pdf
Artikel Jurnal  Universitas Indonesia Library
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Least square support vector machines parameter optimization based on improved ant colony algorithm for hepatitis diagnosis
" Many kinds of classification method are able to diagnose a patient who suffered Hepatitis disease. One of classification methods that can be used was Least Squares Support Vector Machines (LSSVM). There are two parameters that very influence to improve the classification accuracy on LSSVM, they are kernel parameter and regularization parameter. Determining the optimal parameters must be considered to obtain a high classification accuracy on LSSVM. This paper proposed an optimization method based on Improved ... "
Surabaya: Institut Teknologi Sepuluh Nopember, Faculty of Information and Technology, Department of Informatics, 2017
AJ-Pdf
Artikel Jurnal  Universitas Indonesia Library
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Feature selection methods based on mutual information for classifying heterogeneous features
" Datasets with heterogeneous features can affect feature selection results that are not appropriate because it is difficult to evaluate heterogeneous features concurrently. Feature transformation (FT) is another way to handle heterogeneous features subset selection. The results of transformation from non-numerical into numerical features may produce redundancy to the original numerical features. In this paper, we propose a method to select feature subset based on mutual information (MI) for classifying heterogeneous features. We use unsupervised feature ... "
Paiton: STT Nurul Jadid Paiton, Department of Informatics, 2016
AJ-Pdf
Artikel Jurnal  Universitas Indonesia Library