Temat: vector model - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Prediction of protein subcellular localization using support vector machine with the choice of proper kernel
Autorzy:: Hasan, M.A.M.
Ahmad, S.
Molla, M.K.I.
Powiązania:: https://bibliotekanauki.pl/articles/81150.pdf
Data publikacji:: 2017
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: subcellular localization
protein
prediction
support vector machine
model selection
kernel
radial base function
Źródło:: BioTechnologia. Journal of Biotechnology Computational Biology and Bionanotechnology; 2017, 98, 2
0860-7796
Pojawia się w:: BioTechnologia. Journal of Biotechnology Computational Biology and Bionanotechnology
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Traffic fatalities prediction based on support vector machine
Autorzy:: Li, T.
Yang, Y.
Wang, Y.
Chen, C.
Yao, J.
Powiązania:: https://bibliotekanauki.pl/articles/223743.pdf
Data publikacji:: 2016
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: traffic accident
support vector machine
SVM
particle swarm optimization (PSO)
PSO
prediction model
optimal parameters
wypadek drogowy
Particle Swarm Optimization
model prognostyczny
optymalne parametry
Opis:: To effectively predict traffic fatalities and promote the friendly development of transportation, a prediction model of traffic fatalities is established based on support vector machine (SVM). As the prediction accuracy of SVM largely depends on the selection of parameters, Particle Swarm Optimization (PSO) is introduced to find the optimal parameters. In this paper, small sample and nonlinear data are used to predict fatalities of traffic accident. Traffic accident statistics data of China from 1981 to 2012 are chosen as experimental data. The input variables for predicting accident are highway mileage, vehicle number and population size while the output variables are traffic fatality. To verify the validity of the proposed prediction method, the back-propagation neural network (BPNN) prediction model and SVM prediction model are also used to predict the traffic fatalities. The results show that compared with BPNN prediction model and SVM model, the prediction model of traffic fatalities based on PSO-SVM has higher prediction precision and smaller errors. The model can be more effective to forecast the traffic fatalities. And the method using particle swarm optimization algorithm for parameter optimization of SVM is feasible and effective. In addition, this method avoids overcomes the problem of “over learning” in neural network training progress.
Źródło:: Archives of Transport; 2016, 39, 3; 21-30
0866-9546
2300-8830
Pojawia się w:: Archives of Transport
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Classifiers accuracy improvement based on missing data imputation
Autorzy:: Jordanov, I.
Petrov, N.
Petrozziello, A.
Powiązania:: https://bibliotekanauki.pl/articles/91626.pdf
Data publikacji:: 2018
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: machine learning
missing data
model-based imputation
neural networks
random forests
support vector machine
radar signal classification
nauczanie maszynowe
brakujące dane
sieci neuronowe
maszyna wektorów nośnych
klasyfikacja sygnałów radarowych
Opis:: In this paper we investigate further and extend our previous work on radar signal identification and classification based on a data set which comprises continuous, discrete and categorical data that represent radar pulse train characteristics such as signal frequencies, pulse repetition, type of modulation, intervals, scan period, scanning type, etc. As the most of the real world datasets, it also contains high percentage of missing values and to deal with this problem we investigate three imputation techniques: Multiple Imputation (MI); K-Nearest Neighbour Imputation (KNNI); and Bagged Tree Imputation (BTI). We apply these methods to data samples with up to 60% missingness, this way doubling the number of instances with complete values in the resulting dataset. The imputation models performance is assessed with Wilcoxon’s test for statistical significance and Cohen’s effect size metrics. To solve the classification task, we employ three intelligent approaches: Neural Networks (NN); Support Vector Machines (SVM); and Random Forests (RF). Subsequently, we critically analyse which imputation method influences most the classifiers’ performance, using a multiclass classification accuracy metric, based on the area under the ROC curves. We consider two superclasses (‘military’ and ‘civil’), each containing several ‘subclasses’, and introduce and propose two new metrics: inner class accuracy (IA); and outer class accuracy (OA), in addition to the overall classification accuracy (OCA) metric. We conclude that they can be used as complementary to the OCA when choosing the best classifier for the problem at hand.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2018, 8, 1; 31-48
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

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