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LIBTwinSVM: A Library for Twin Support Vector Machines [article]

Amir M. Mir, Mahdi Rahbar, Jalal A. Nasiri
2020 arXiv   pre-print
This paper presents LIBTwinSVM, a free, efficient, and open source library for Twin Support Vector Machines (TSVMs).  ...  The benchmarks results indicate the effectiveness of the LIBTwinSVM library for large-scale classification problems.  ...  We would like to thank the Director of the IranDoc Institution for providing us research facilities. Appendix A. Additional Experiments  ... 
arXiv:2001.10073v1 fatcat:bu2iuv4n6bhozmdyrxb4z75r5m

EnsembleSVM: A Library for Ensemble Learning Using Support Vector Machines [article]

Marc Claesen, Frank De Smet, Johan Suykens, Bart De Moor
2014 arXiv   pre-print
EnsembleSVM is a free software package containing efficient routines to perform ensemble learning with support vector machine (SVM) base models.  ...  Our implementation avoids duplicate storage and evaluation of support vectors which are shared between constituent models.  ...  Base model flexibility is maximized by using instance-weighted binary support vector machine classifiers, as defined in Equation (1) .  ... 
arXiv:1403.0745v1 fatcat:7znyse5xejd6je4vxmpoaa6kse

LIBSVM

Chih-Chung Chang, Chih-Jen Lin
2011 ACM Transactions on Intelligent Systems and Technology  
LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications.  ...  LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM.  ...  The authors thank their group members and users for many helpful comments. A list of acknowledgments is at http://www.csie.ntu.edu.tw/~cjlin/libsvm/acknowledgements.  ... 
doi:10.1145/1961189.1961199 fatcat:vwus35v7vbhatbrli3wuems5h4

LIBSVM : A Library for Support Vector Machines

C Chang, C Lin
2011 ACM Transactions on Intelligent Systems and Technology   unpublished
Vector ‫ﺧﻮدﮐﺎر‬ ‫اﺳﺘﺨﺮاج‬ ‫ﮐـﺮ‬ ‫اﻋﻤـﺎل‬ ‫ﺼـﺎوﯾﺮ‬ ‫ﻪ‬ ‫اﻧﺪ‬ . ‫  ...  ﺧﻮدرو‬ ‫ﻣﺪل‬ ‫ﺎ‬ ‫ﻧﺸـﺪ‬ ‫اراﺋـﻪ‬ ‫ﺣـﻮزه‬ ‫داده‬ ‫ﻣﺠﻤﻮﻋـﻪ‬ ‫ی‬ ‫ﺗﻬﯿـ‬ ‫داده‬ ‫ﺠﻤﻮﻋـﻪ‬ ‫ﻣـﺎه‬ ‫ﺷـﺶ‬ ‫ﻣـﺪت‬ ‫ﻧﻘﻠﯿـ‬ ‫وﺳﺎﯾﻞ‬ ‫از‬ ‫ﻒ‬ ‫ﻇﻬـﺮ‬ ‫ﺻـﺒﺢ،‬ ‫ﯽ‬ ‫ﺷـﻮﻧﺪ‬ ‫ﺷـﺎﻣﻞ‬ ‫را‬ Histogram of O Support  ... 
fatcat:plpwlrdeofetrnwi7dfhvopx3u

Anomaly Detection Using LibSVM Training Tools

Chu-Hsing Lin, Jung-Chun Liu, Chia-Han Ho
2008 2008 International Conference on Information Security and Assurance (isa 2008)  
In this research, we use support vector machine as a learning method for anomaly detection, and use LibSVM as the support vector machine tool.  ...  In recent years, learning machine technology is often used as a detection method in anomaly detection.  ...  LibSVM LibSVM is a library for support vector machines. Its goal is to promote SVM as a convenient tool.  ... 
doi:10.1109/isa.2008.12 fatcat:nv54zfss7zbvdldadalwmpifma

EEG based Emotion Recognition using SVM and LibSVM

Aadhya Bhatt, Ananta Bhatt
2019 International Journal of Computer Applications  
By using library LibSVM (3.23), we increased the performance of each run by 4% finally resulting with 79.38% accuracy having tensor flow environment.  ...  Using SVM classifier with external library LibSVM (3.23), we have classified our EEG SEED dataset and have achieved tremendous improvement in the accuracy and performance.  ...  Fig. 1: Detailed Diagram for Dataset collection This paper incorporates SVM with LibSVM (3.23) library tool extension, an integral software for support vector classification.  ... 
doi:10.5120/ijca2019919352 fatcat:ltzdju3zrnafxpbzpwrpnsbera

Time Complexity Analysis of Support Vector Machines (SVM) in LibSVM

Abdiansah Abdiansah, Retantyo Wardoyo
2015 International Journal of Computer Applications  
Support Vector Machines (SVM) is one of machine learning methods that can be used to perform classification task. Many researchers using SVM library to accelerate their research development.  ...  The library also integrated to WEKA, one of popular Data Mining tools. This article contain results of our work related to complexity analysis of Support Vector Machines.  ...  Support Vector Machines Support Vector Machines (SVM) is one of machine learning algorithms using supervised learning models for pattern recognition.  ... 
doi:10.5120/ijca2015906480 fatcat:f26zowvnazhrffaucxayohrbxi

Bronchopulmonary Dysplasia Prediction Using Support Vector Machine and LIBSVM

Marcin Ochab, Wiesław Wajs
2014 Proceedings of the 2014 Federated Conference on Computer Science and Information Systems  
SVM (Support Vector Machine) algorithm implemented in LIBSVM[1] was used as classifier.  ...  The paper presents BPD (Bronchopulmonary Dysplasia) prediction for extremely premature infants after their first week of life.  ...  As a final conclusion we confirmed [32] that prediction of BPD after 7th day of life is possible with the accuracy higher than 82%, not only with LR but also using Support Vector Machine algorithm.  ... 
doi:10.15439/2014f111 dblp:conf/fedcsis/OchabW14 fatcat:3t4gafr6zratjba54sgskn7sgq

GURLS vs LIBSVM: Performance Comparison of Kernel Methods for Hyperspectral Image Classification

Nikhila Haridas, V. Sowmya, K. P. Soman
2015 Indian Journal of Science and Technology  
The proposed work compares the performance of different kernel methods available in GURLS package with the library for Support Vector Machines namely, LIBSVM.  ...  This paper introduces a new kernel based framework for Hyper Spectral Image (HSI) classification using Grand Unified Regularized Least Squares (GURLS) library.  ...  LIBSVM LIBSVM 12 is the most popular software library for Support Vector Machines (SVM).  ... 
doi:10.17485/ijst/2015/v8i24/80843 fatcat:3mwzv24x35ho3hnhuv5x4sc44e

The Short-term Predicting Method of Algal Blooms Based on Libsvm and Elman Neural Network Modeling

Mengxun Li, Zaiwen Liu Wei, Hua, Xue Zhang, Chengrui Wu
2013 Sensors & Transducers  
On this basis, to take advantage of the Libsvm water bloom prediction model and Elman water bloom prediction model for the short-term prediction of algal blooms phenomenon respectively.  ...  Obtained through the fitting networks in the long-term forecasting of algal blooms, the Libsvm prediction accuracy is much higher than the prediction accuracy of artificial neural network.  ...  The Study of Libsvm Support Vector Machine-based on the Short-term Prediction of Water Bloom Water Bloom Prediction Model Based on the Libsvm Model Support Vector Machine (for short SVM) is a new machine  ... 
doaj:eb595b9eab89414bb83e5e2d1910692f fatcat:yo5mqvaqfncghnckeiw6t3qywi

Recognition of Oil Shale Based on LIBSVM Optimized by Modified Genetic Algorithm

Qihua Hu, Cong Wang, Xin Zhang, Jingjing Fan
2015 Open Petroleum Engineering Journal  
According to the popularization effects in the well area of same geology background, this optimized LIBSVM model is the best for now.  ...  In order to improved the speed, accuracy and generalization of oil shale recognition model with log dada, considering parameters of traditional SVM were chosen by experience, a LIBSVM recognition model  ...  Therefore, combined with other methods, a method based on a Library for Support Vector Machine (LIBSVM) modified by genetic algorithm was promoted to identify oil shale.  ... 
doi:10.2174/1874834101508010363 fatcat:sdhsghrgozaw7p6yhlbzdbstoy

A Method for Identifying Vesicle Transport Proteins Based on LibSVM and MRMD

Zhiyu Tao, Yanjuan Li, Zhixia Teng, Yuming Zhao, Hui Ding
2020 Computational and Mathematical Methods in Medicine  
We adopt CTDC which is a submethod of the method of composition, transition, and distribution (CTD) to extract only 39 features from each sequence, and LibSVM is used as the classification method.  ...  With the development of computer technology, many machine learning algorithms have been applied to the field of biology, forming the discipline of bioinformatics.  ...  Acknowledgments This work was supported in part by the National Natural Science Foundation of China (grant numbers 61971117 and 61901103) and in part by the Natural Science Foundation of Heilongjiang Province  ... 
doi:10.1155/2020/8926750 pmid:33133228 pmcid:PMC7591939 fatcat:4zmv6rmhp5a6fe22hywyyvnx7e

Looking into the green roof scenario to mitigate flash flood effects in Mamak, Turkey, via classifying images of Sentinel-1, 2, and PlanetScope satellites with LibSVM algorithm in Google Earth Engine cloud platform

Sima Pouya, Majid Aghlmand, Fevzi Karsli
2022 Geografie-Sbornik CGS  
This study was proposed as a solution for the flood disaster, using the extensive green roof scenario. After green roof conversion scenarios, the GSF value was recalculated.  ...  The land use/cover map was first obtained by using the images of Sentinel-1, Sentinel-2, and PlanetScope satellites with the LIBSVM algorithm on the Google Earth Engine.  ...  In order to that, land use classification map was generated using remote sensing satellite images on the Google Earth Engine platform using the LIBSVM (A Library for Support Vector Machines) algorithm.  ... 
doi:10.37040/geografie.2022.008 fatcat:gg6crrrycbbmzikcbse5d3atwe

A Reconfigurable Multiclass Support Vector Machine Architecture for Real-Time Embedded Systems Classification

Jason Kane, Robert Hernandez, Qing Yang
2015 2015 IEEE 23rd Annual International Symposium on Field-Programmable Custom Computing Machines  
-Support up to a maximum #Classes/Features, specified at compilation -Allow for targeting of diverse workloads • Develop a prototype compatible with libsvm to provide direct performance comparisons  ...  Neither support more than 2 classes. -We use a private library provided by the author of [3] .• FPGA Based -Mostly implementation specific designs.-None that support multi-class.  ... 
doi:10.1109/fccm.2015.24 dblp:conf/fccm/KaneHY15 fatcat:6pr3oo6vljchfmm5vcdjwb2mku

ThunderSVM: A Fast SVM Library on GPUs and CPUs

Zeyi Wen, Jiashuai Shi, Qinbin Li, Bingsheng He, Jian Chen
2018 Journal of machine learning research  
Support Vector Machines (SVMs) are classic supervised learning models for classification, regression and distribution estimation.  ...  A survey conducted by Kaggle in 2017 shows that 26% of the data mining and machine learning practitioners are users of SVMs.  ...  Acknowledgments This work is supported by a MoE AcRF Tier 1 grant (T1 251RES1610) and Tier 2 grant (MOE2017-T2-1-122) in Singapore. Prof.  ... 
dblp:journals/jmlr/WenSLHC18 fatcat:ruczlgi345e35nffjwwdv5dlwe
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