An approximation of the Gaussian RBF kernel for efficient classification with SVMs

Ring, Matthias; Eskofier, Bjoern M.

doi:10.1016/j.patrec.2016.08.013

Cited by 83 publications

(37 citation statements)

References 9 publications

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“…The mean grayscale value and the statistical features describing the distribution of the gray level were extracted to represent the grayscale features, including mean grayscale value, maximum Radial basis function (RBF) kernel is the most widely applied kernel in SVM, which is well known for its excellent performance in pattern classification and function approximation [31]. In this study, we obtained the optimum values of the penalty parameter c and the kernel parameter g via grid search.…”

Section: Feature Extractionmentioning

confidence: 99%

Intelligent Estimation of Vitrinite Reflectance of Coal from Photomicrographs Based on Machine Learning

Wang

Lei

et al. 2019

Energies

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The accurate measurement of vitrinite reflectance (especially for mean maximum vitrinite reflectance, MMVR) is an important issue in the fields of coal mining and processing. However, the application of MMVR has been somewhat hampered by the subjective and the time-consuming characteristic of manual measurements. Semi-automated methods that are oversimplified might affect the accuracy in measuring MMVR values. To address these concerns, we propose a novel MMVR measurement strategy based on machine learning (MMVRML). Considering the complex nature of coal, adaptive K-means clustering is firstly employed to automatically detect the number of clusters (i.e., maceral groups) in photomicrographs. Furthermore, comprehensive features along with a support vector machine are utilized to intelligently identify the regions with vitrinite. The largest region with vitrinite in each photomicrograph is gridded for further regression analysis. Evaluations on 78 photomicrographs show that the model based on random forest and 15 simplified grayscale features achieves the state-of-the-art root mean square error of 0.0424. In addition, to facilitate the usage of petrologists without strong expertise in the machine learning domain, we released the first non-commercial standalone software for estimating MMVR.

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Section: Feature Extractionmentioning

confidence: 99%

Intelligent Estimation of Vitrinite Reflectance of Coal from Photomicrographs Based on Machine Learning

Wang

Lei

et al. 2019

Energies

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“…Mathias Ring dkk. menggunakan Kernel Gaussian RBF untuk SVM sehingga menghasilkan waktu proses yang lebih baik tanpa kehilangan akurasi [16]. Bissan Ghaddar dan Joe Naoum-Sawaya menguji SVM terhadap kasus sentimen film dan klasifikasi penyakit.…”

Section: B Support Vector Machinesunclassified

Peringkasan dan Support Vector Machine pada Klasifikasi Dokumen

Widiastuti

Rainarli²,

Dewi³

2017

INFOTEL

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Abstrak -Klasifikasi adalah proses pengelompokkan objek yang memiliki karakteristik atau ciri yang sama ke dalam beberapa kelas. Klasifikasi dokumen secara otomatis dapat dilakukan dengan menggunakan ciri atau fitur kata yang muncul pada dokumen latih. Jumlah dokumen yang besar dan banyak mengakibatkan jumlah kata yang muncul sebagai fitur akan bertambah. Oleh karena itu, peringkasan dipilih untuk mereduksi jumlah kata yang digunakan dalam proses klasifikasi. Untuk proses klasifikasi digunakan metode Support Vector Machine (SVM) untuk multikelas. SVM dipilih karena dianggap memiliki reputasi yang baik dalam klasifikasi. Penelitian ini menguji penggunaan ringkasan sebagai seleksi fitur dalam klasifikasi dokumen. Peringkasan menggunakan kompresi 50 %. Hasil yang diperoleh menunjukkan bahwa proses peringkasan tidak mempengaruhi nilai akurasi dari klasifikasi dokumen yang menggunakan SVM.Akan tetapi, penggunaan peringkasan berpengaruh pada peningkatan hasil akurasi dari metode klasifikasiSimple Logistic Classifier (SLC). Hasil pengujian metode klasifikasi menunjukkan bahwa penggunaan metode Naïve Bayes Multinomial (NBM) menghasilkan akurasi yang lebih baik dari pada metode SVM.Kata kunci -peringkasan, klasifikasi, SVM, seleksi fitur, dokumen Abstract -Classification is the process of grouping objects that have the same features or characteristics into several classes. The automatic documents classification use words frequency that appears on training data as features.The large number of documents cause the number of words that appears as a feature will increase.Therefore, summaries are chosen to reduce the number of words that used in classification.The classification uses multiclass Support Vector Machine (SVM) method.SVM was considered to have a good reputation in the classification. This research tests the effect of summary as selection features into documents classification.The summariesreduce text into 50%.A result obtained that the summaries did not affect value accuracy of classification of documents that use SVM.But, summaries improve the accuracy of Simple Logistic Classifier. The classification testing shows that the accuracy of Naïve Bayes Multinomial (NBM) better than SVM.

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“…SVM can achieve good accuracy with a sufficiently large training set. Some kernel functions can make it possible to solve the nonlinear problem in high dimensional space [ 35 , 36 , 37 , 38 ]. One popular kernel used in SVM is RBF (RBFSVM).…”

Section: Introductionmentioning

confidence: 99%

A Video Based Fire Smoke Detection Using Robust AdaBoost

Leung

2018

Sensors

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This work considers using camera sensors to detect fire smoke. Static features including texture, wavelet, color, edge orientation histogram, irregularity, and dynamic features including motion direction, change of motion direction and motion speed, are extracted from fire smoke to train and test with different combinations. A robust AdaBoost (RAB) classifier is proposed to improve training and classification accuracy. Extensive experiments on well known challenging datasets and application for fire smoke detection demonstrate that the proposed fire smoke detector leads to a satisfactory performance.

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An approximation of the Gaussian RBF kernel for efficient classification with SVMs

Cited by 83 publications

References 9 publications

Intelligent Estimation of Vitrinite Reflectance of Coal from Photomicrographs Based on Machine Learning

Intelligent Estimation of Vitrinite Reflectance of Coal from Photomicrographs Based on Machine Learning

Peringkasan dan Support Vector Machine pada Klasifikasi Dokumen

A Video Based Fire Smoke Detection Using Robust AdaBoost

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