A New Method of Training Sample Selection in Text Classification

2010 Second International Workshop on Education Technology and Computer Science ◽

10.1109/etcs.2010.621 ◽

2010 ◽

Cited By ~ 2

Author(s):

Yixing Liao ◽

Xuezeng Pan

Keyword(s):

Text Classification ◽

Sample Selection ◽

Training Sample ◽

New Method ◽

Training Sample Selection

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A text classification model based on training sample selection and feature weight adjustement

2010 2nd International Conference on Advanced Computer Control ◽

10.1109/icacc.2010.5486615 ◽

2010 ◽

Cited By ~ 1

Author(s):

Xuezeng Pang ◽

Yixing Liao

Keyword(s):

Text Classification ◽

Sample Selection ◽

Training Sample ◽

Classification Model ◽

Model Based ◽

Feature Weight ◽

Training Sample Selection

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Training sample selection: impact on screening automation in diagnostic test accuracy reviews

Research Synthesis Methods ◽

10.1002/jrsm.1518 ◽

2021 ◽

Author(s):

A. J. Altena ◽

R. Spijker ◽

M. M. G. Leeang ◽

S. D. Olabarriaga

Keyword(s):

Diagnostic Test ◽

Sample Selection ◽

Training Sample ◽

Diagnostic Test Accuracy ◽

Test Accuracy ◽

Training Sample Selection

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Active learning for training sample selection in remote sensing image classification using spatial information

Remote Sensing Letters ◽

10.1080/2150704x.2017.1375610 ◽

2017 ◽

Vol 8 (12) ◽

pp. 1210-1219 ◽

Cited By ~ 7

Author(s):

Qikai Lu ◽

Yong Ma ◽

Gui-Song Xia

Keyword(s):

Remote Sensing ◽

Active Learning ◽

Image Classification ◽

Spatial Information ◽

Sample Selection ◽

Remote Sensing Image ◽

Training Sample ◽

Remote Sensing Image Classification ◽

Training Sample Selection

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Active training sample selection and updating strategy for near-infrared model with an industrial application

Chinese Journal of Chemical Engineering ◽

10.1016/j.cjche.2019.02.018 ◽

2019 ◽

Vol 27 (11) ◽

pp. 2749-2758

Author(s):

Kaixun He ◽

Kai Wang ◽

Yayun Yan

Keyword(s):

Industrial Application ◽

Near Infrared ◽

Sample Selection ◽

Training Sample ◽

Active Training ◽

Training Sample Selection

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Cyclic training sample selection and cancellation technique for airborne STAP radar under nonhomogeneous environment

Digital Signal Processing ◽

10.1016/j.dsp.2020.102803 ◽

2020 ◽

Vol 104 ◽

pp. 102803

Author(s):

Xinzhe Li ◽

Wenchong Xie ◽

Yongliang Wang

Keyword(s):

Sample Selection ◽

Training Sample ◽

Training Sample Selection

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Training Sample Selection in Learning Control

2004 IEEE International Conference on Robotics and Biomimetics ◽

10.1109/robio.2004.1521806 ◽

2005 ◽

Author(s):

Jun Cheng ◽

Yangsheng Xu ◽

R. Chung

Keyword(s):

Sample Selection ◽

Learning Control ◽

Training Sample ◽

Training Sample Selection

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Analysis of training sample selection strategies for regression-based quantitative landslide susceptibility mapping methods

Computers & Geosciences ◽

10.1016/j.cageo.2017.03.022 ◽

2017 ◽

Vol 104 ◽

pp. 62-74 ◽

Cited By ~ 9

Author(s):

Arzu Erener ◽

A. Abdullah Sivas ◽

A. Sevtap Selcuk-Kestel ◽

H. Sebnem Düzgün

Keyword(s):

Landslide Susceptibility ◽

Sample Selection ◽

Training Sample ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Selection Strategies ◽

Training Sample Selection

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Training sample selection for space–time adaptive processing based on multi-frames

The Journal of Engineering ◽

10.1049/joe.2019.0410 ◽

2019 ◽

Vol 2019 (20) ◽

pp. 6369-6372

Author(s):

Chenxiao Zhang ◽

Yifeng Wu ◽

Mingming Guo ◽

Xiaobo Deng

Keyword(s):

Sample Selection ◽

Training Sample ◽

Space Time ◽

Adaptive Processing ◽

Selection For ◽

Space Time Adaptive Processing ◽

Training Sample Selection

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An Iterative Random Training Sample Selection Approach to Constrained Energy Minimization for Hyperspectral Image Classification

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2020.3005078 ◽

2020 ◽

pp. 1-5

Author(s):

Xiaodi Shang ◽

Meiping Song ◽

Chein-I Chang

Keyword(s):

Image Classification ◽

Energy Minimization ◽

Hyperspectral Image ◽

Sample Selection ◽

Training Sample ◽

Hyperspectral Image Classification ◽

Selection Approach ◽

Training Sample Selection

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Improving SVM Training Sample Selection Using Multi-Objective Evolutionary Algorithm and LSH

2015 IEEE Symposium Series on Computational Intelligence ◽

10.1109/ssci.2015.197 ◽

2015 ◽

Cited By ~ 2

Author(s):

Romaric Pighetti ◽

Denis Pallez ◽

Frederic Precioso

Keyword(s):

Evolutionary Algorithm ◽

Sample Selection ◽

Training Sample ◽

Multi Objective ◽

Training Sample Selection

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