New Method Based on Support Vector Machine in Classification for Hyperspectral Data

2008 International Symposium on Computational Intelligence and Design ◽

10.1109/iscid.2008.61 ◽

2008 ◽

Author(s):

Xiangtao Wang ◽

Yan Feng

Keyword(s):

Support Vector Machine ◽

Hyperspectral Data ◽

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A New Method to Obtain 3-D Surface Deformations From InSAR and GNSS Data With Genetic Algorithm and Support Vector Machine

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2021.3049128 ◽

2021 ◽

pp. 1-5

Author(s):

Panfeng Ji ◽

Xiaolei Lv ◽

Jingchuan Yao ◽

Guangcai Sun

Keyword(s):

Genetic Algorithm ◽

Support Vector Machine ◽

Support Vector ◽

Surface Deformations ◽

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New method for solving Ivanov regularization-based support vector machine learning

Computers & Operations Research ◽

10.1016/j.cor.2021.105504 ◽

2021 ◽

pp. 105504

Author(s):

Xiang Xu ◽

Daoli Zhu

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

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A new method for the segmentation of algae images using retinex and support vector machine

2015 IEEE International Conference on Electro/Information Technology (EIT) ◽

10.1109/eit.2015.7293369 ◽

2015 ◽

Author(s):

Kyle Dannemiller ◽

Kaveh Ahmadi ◽

Ezzatollah Salari

Keyword(s):

Support Vector Machine ◽

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Landuse classification of hyperspectral data by spectral angle mapper and support vector machine in humid tropical region of India

Earth Science Informatics ◽

10.1007/s12145-019-00438-4 ◽

2020 ◽

Vol 13 (3) ◽

pp. 633-640 ◽

Author(s):

Girish Gopinath ◽

Nimmi Sasidharan ◽

U. Surendran

Keyword(s):

Support Vector Machine ◽

Hyperspectral Data ◽

Support Vector ◽

Tropical Region ◽

Spectral Angle Mapper ◽

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Comparison of partial least square regression, support vector machine, and deep-learning techniques for estimating soil salinity from hyperspectral data

Journal of Applied Remote Sensing ◽

10.1117/1.jrs.12.022204 ◽

2018 ◽

Vol 12 (02) ◽

pp. 1 ◽

Author(s):

Wenzhi Zeng ◽

Dongying Zhang ◽

Yuanhao Fang ◽

Jingwei Wu ◽

Jiesheng Huang

Keyword(s):

Support Vector Machine ◽

Deep Learning ◽

Soil Salinity ◽

Hyperspectral Data ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Support Vector ◽

Least Square Regression ◽

Learning Techniques

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Comparison of Support Vector Machine, Artificial Neural Networks and Spectral Angle Mapper Classifiers on Fused Hyperspectral Data for Improved LULC Classification

2019 8th International Conference on Modeling Simulation and Applied Optimization (ICMSAO) ◽

10.1109/icmsao.2019.8880336 ◽

2019 ◽

Author(s):

Veeramallu Satya Sahithi ◽

Stalin Subbiah ◽

Shefali Agrawal

Keyword(s):

Neural Networks ◽

Support Vector Machine ◽

Artificial Neural Networks ◽

Hyperspectral Data ◽

Support Vector ◽

Spectral Angle Mapper ◽

Artificial Neural ◽

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A new method to improve the sensitivity of support vector machine based on data optimization

IEEE International Conference on Robotics, Intelligent Systems and Signal Processing, 2003. Proceedings. 2003 ◽

10.1109/rissp.2003.1285705 ◽

2004 ◽

Author(s):

Zhan Yong ◽

Zhou Yan-hong ◽

Lu Zheng-ding

Keyword(s):

Support Vector Machine ◽

Support Vector ◽

Data Optimization

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Using a Feature Subset Selection method and Support Vector Machine to address curse of dimensionality and redundancy in Hyperion hyperspectral data classification

The Egyptian Journal of Remote Sensing and Space Science ◽

10.1016/j.ejrs.2017.02.003 ◽

2018 ◽

Vol 21 (1) ◽

pp. 27-36 ◽

Author(s):

Amir Salimi ◽

Mansour Ziaii ◽

Ali Amiri ◽

Mahdieh Hosseinjani Zadeh ◽

Sadegh Karimpouli ◽

...

Keyword(s):

Support Vector Machine ◽

Subset Selection ◽

Data Classification ◽

Curse Of Dimensionality ◽

Selection Method ◽

Hyperspectral Data ◽

Feature Subset Selection ◽

Support Vector ◽

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Wavelet texture extraction and image classification of hyperspectral data based on Support Vector Machine

2009 17th International Conference on Geoinformatics ◽

10.1109/geoinformatics.2009.5292863 ◽

2009 ◽

Author(s):

Jin-Tsong Hwang

Keyword(s):

Support Vector Machine ◽

Image Classification ◽

Hyperspectral Data ◽

Support Vector ◽

Texture Extraction

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An New Fuzzy Support Vector Machine for Binary Classification

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.433-440.2856 ◽

2012 ◽

Vol 433-440 ◽

pp. 2856-2861 ◽

Author(s):

Rui Zhang ◽

Tong Bo Liu ◽

Ming Wen Zheng

Keyword(s):

Support Vector Machine ◽

Binary Classification ◽

Experimental Results ◽

Support Vector ◽

Fuzzy Support Vector Machine

In this paper, we proposed a new fuzzy support vector machine(called L2–FSVM here), which error part of object is L2–norm.Meanwhile we introduce a new method of generating fuzzy memberships so as to reduce to effects of outliers. The experimental results demonstrate that the L2-FSVM method provides improved ability to reduce to effects of outliers in comparison with traditional SVMs and FSVMs, and claim that L2–FSVM is the best way to solve the binary classiﬁcation in the three methods stated above.

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