Prediction based on support vector machine for travel choice of high-speed railway passenger in China

Shu, Kang; Li, Jing; Liu, Mei; Zhu, Xin

doi:10.1109/icmse.2011.6069938

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…Prediksi ini menggunakan metode SVM dimana hasil akurasi yang didapatkan adalah 91.44% [Madzarov, Gjorgji, Gjorgjevikj, Dejan, & Chorbev, Ivan, 2008]. Kemudian penelitian lainnya yaitu "Detect Pedestrian Orientation by Integrating Multiclass SVM Utilizing Binary Decision Tree" [Shu, K., Jing, L., Mei, L., Xin, Z., 2011]. Penelitian ini untuk memprediksi kemungkinan terjadi tabrakan dengan mendeteksi arah pejalan kaki menggunakan metode yang sama yaitu binary decision tree-SVM menggunakan dataset INRIA yang terdiri dari 2000 gambar.…”

Section: Pendahuluanunclassified

Perbandingan Metode SVM, FUZZY-KNN, Dan BDT-SVM Untuk Klasifikasi Detak Jantung Hasil Elektrokardiografi

Hasanah

Mayangsari

Pratama

et al. 2016

JTIIK

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Section: Pendahuluanunclassified

Perbandingan Metode SVM, FUZZY-KNN, Dan BDT-SVM Untuk Klasifikasi Detak Jantung Hasil Elektrokardiografi

Hasanah

Mayangsari

Pratama

et al. 2016

JTIIK

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show abstract

“…The basic idea of SVM is making a description about multidimensional and complex variable through the black-box model [19] . The vectors are non-linearly mapped to a very high dimension feature space.…”

Section: Theoretical Fundamental 21 Introduction Of Svmmentioning

confidence: 99%

Prediction of DC Corona Onset Voltage for Rod-Plane Air Gaps by a Support Vector Machine

Jin

Ruan

Zhang

et al. 2016

Plasma Sci. Technol.

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This paper proposes a new method to predict the corona onset voltage for a rodplane air gap, based on the support vector machine (SVM). Because the SVM is not limited by the size, dimension and nonlinearity of the samples, this method can realize accurate prediction with few training data. Only electric field features are chosen as the input; no geometric parameter is included. Therefore, the experiment data of one kind of electrode can be used to predict the corona onset voltages of other electrodes with different sizes. With the experimental data obtained by ozone detection technology, and experimental data provided by the reference, the efficiency of the proposed method is validated. Accurate predicted results with an average relative less than 3% are obtained with only 6 experimental data.

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Short-Term Subway Passenger Flow Prediction Based on ARIMA

Yan

Zhou

Zhao

et al. 2018

Communications in Computer and Information Science

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Prediction based on support vector machine for travel choice of high-speed railway passenger in China

Cited by 4 publications

References 3 publications

Perbandingan Metode SVM, FUZZY-KNN, Dan BDT-SVM Untuk Klasifikasi Detak Jantung Hasil Elektrokardiografi

Perbandingan Metode SVM, FUZZY-KNN, Dan BDT-SVM Untuk Klasifikasi Detak Jantung Hasil Elektrokardiografi

Prediction of DC Corona Onset Voltage for Rod-Plane Air Gaps by a Support Vector Machine

Short-Term Subway Passenger Flow Prediction Based on ARIMA

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