Vector machine techniques for modeling of seismic liquefaction data
Ain Shams Engineering Journal, 2014 This article employs three soft computing techniques, Support Vector Machine (SVM); Least Square Support Vector Machine (LSSVM) and Relevance Vector Machine (RVM), for prediction of liquefaction susceptibility of soil.
Pijush Samui
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Linear Classification of Data with Support Vector Machines and Generalized Support Vector Machines [PDF]
, 2016 submitted
Qi, Xiaomin +2 more
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Doubly Optimized Calibrated Support Vector Machine (DOC-SVM): an algorithm for joint optimization of discrimination and calibration. [PDF]
, 2012 Historically, probabilistic models for decision support have focused on discrimination, e.g., minimizing the ranking error of predicted outcomes. Unfortunately, these models ignore another important aspect, calibration, which indicates the magnitude of ...
Jiang, Xiaoqian +4 more
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Hierarchical linear support vector machine [PDF]
, 2012 This is the author’s version of a work that was accepted for publication in Pattern Recognition. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not
Huerta, Ramón +2 more
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Gaussian Pyramid for Nonlinear Support Vector Machine
Applied Computational Intelligence and Soft Computing, 2022 Support vector machine (SVM) is one of the most efficient machine learning tools, and it is fast, simple to use, reliable, and provides accurate classification results.
Rawan Abo Zidan, George Karraz
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$$\nu $$ ν -Improved nonparallel support vector machine
Scientific Reports, 2022 In this paper, a $$\nu $$ ν -improved nonparallel support vector machine ( $$\nu $$ ν -IMNPSVM) is proposed to solve binary classification problems.
Fengmin Sun, Shujun Lian
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Multitraining support vector machine for image retrieval [PDF]
, 2006 Relevance feedback (RF) schemes based on support vector machines (SVMs) have been widely used in content-based image retrieval (CBIR). However, the performance of SVM-based RF approaches is often poor when the number of labeled feedback samples is small.
Allinson, N. +3 more
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Penerapan Metode Support Vector Machine pada Sistem Deteksi Intrusi secara Real-time [PDF]
, 2013 Intrusion detection system is a system for detecting attacks or intrusions in a network or computer system, generally intrusion detection is done with comparing network traffic pattern with known attack pattern or with finding unnormal pattern of network
Jacobus, J. (Jacobus), Winarko, E. (Edi)
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Machine learning technique for morphological classification of galaxies at z<0.1 from the SDSS
, 2020 Methods. We used different galaxy classification techniques: human labeling, multi-photometry diagrams, Naive Bayes, Logistic Regression, Support Vector Machine, Random Forest, k-Nearest Neighbors, and k-fold validation. Results.
Dobrycheva, D. V. +5 more
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Support Vector Machine for Network Intrusion and Cyber-Attack Detection [PDF]
, 2017 The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Cyber-security threats are a growing concern in networked environments.
Aparicio-Navarro, Francisco J. +4 more
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