최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기Expert systems with applications, v.139, 2020년, pp.112865 -
Kim, Dongil (Department of Computer Science & Engineering, Chungnam National University) , Kang, Seokho (Corresponding author.) , Cho, Sungzoon (Department of Industrial Engineering & Institute for Industrial Systems Innovation, Seoul National University)
Abstract Support Vector Machines (SVMs) are amongst the most powerful classification algorithms in machine learning and data mining. However, SVMs are limited by high training complexity when training with large datasets. Pattern selection methods have been proposed to reduce the training complexit...
Almeida 162 2000 Proceedings of the 6th brazilian symposium on neural networks SVM-KM: Speeding SVMs learning with a priori cluster selection and k-means
IEEE Transactions on Semiconductor Manufacturing Baly 25 3 373 2012 10.1109/TSM.2012.2196058 Wafer classification using support vector machines
Besnard 2006 Proceedings of the 3rd ismi symposium on manufacturing effectiveness Wafer-to-wafer virtual metrology applied to run-to-run control
Expert Systems with Applications Bosch 40 10 4029 2013 10.1016/j.eswa.2013.01.006 Support vector machine under uncertainty: An application for hydroacoustic classification of fish-schools in Chile
Data Mining and Knowledge Discovery Burges 2 121 1998 10.1023/A:1009715923555 A tutorial on support vector machines for pattern recognition
ACM Transactions on Intelligent Systems and Technology Chang 2 3 27 2011 10.1145/1961189.1961199 LIBSVM: A library for support vector machines
Pattern Recognition Letters Guo 28 2173 2007 10.1016/j.patrec.2007.04.017 Reducing examples to accelerate support vector regression
Pattern Recognition Letters Guo 51 112 2015 10.1016/j.patrec.2014.08.003 Fast data selection for SVM training using ensemble margin
Neurocomputing He 74 10 1585 2011 10.1016/j.neucom.2011.01.019 Neighborhood based sample and feature selection for SVM classification learning
Hsieh 408 2008 Proceedings of the 2008 international conference on machine learning A dual coordinate descent method for large-scale linear SVM
Applied Soft Computing Jedliski 30 636 2015 10.1016/j.asoc.2015.02.015 Early fault detection in gearboxes based on support vector machines and multilayer perceptron with a continuous wavelet transform
Joachims 1998 Technical Report Making large-scale SVM learning practical
Expert Systems with Applications Kang 51 85 2016 10.1016/j.eswa.2015.12.027 Semi-supervised support vector regression based on self-training with label uncertainty: An application to virtual metrology in semiconductor manufacturing
Expert Systems with Applications Kang 38 3 2508 2011 10.1016/j.eswa.2010.08.040 Virtual metrology for run-to-run control in semiconductor manufacturing
Lecture Notes in Computer Science Kawulok 7626 557 2012 10.1007/978-3-642-34166-3_61 Support vector machines training data selection using a genetic algorithm
Expert Systems with Applications Kim 39 10 8975 2012 10.1016/j.eswa.2012.02.026 Pattern selection for support vector regression based response modeling
Expert Systems with Applications Kim 39 4 4075 2012 10.1016/j.eswa.2011.09.088 Machine learning-based novelty detection for faulty wafer detection in semiconductor manufacturing
IEEE Transactions on Pattern Analysis and Machine Intelligence Li 33 6 1189 2011 10.1109/TPAMI.2010.188 Selecting critical patterns based on local geometrical and statistical information
Applied Soft Computing Maldonado 25 740 2015 10.1016/j.asoc.2015.05.058 Profit-based feature selection using support vector machines - general framework and an application for customer retention
IEEE Transactions on Neural Networks Mangasarian 10 5 1032 1999 10.1109/72.788643 Successive overrelaxation for support vector machines
Pattern Recognition Maulik 44 3 615 2011 10.1016/j.patcog.2010.09.021 A self-trained ensemble with semisupervised SVM: An application to pixel classification of remote sensing imagery
Nghi 28 2011 Proceedings of the international conference on information and electronics engineering Training data selection for support vector machines model
Omega Pai 39 6 497 2005 10.1016/j.omega.2004.07.024 A hybrid ARIMA and support vector machines model in stock price forecasting
Platt 41 1998 Advances in kernel methods - Support Vector Learning Fast training of support vector machines using sequential minimal optimization
Information Sciences Ramirez 237 59 2013 10.1016/j.ins.2009.05.012 Computer-aided diagnosis of alzheimer’s type dementia combining support vector machines and discriminant set of features
Mathematical Programming Shalev-Shwartz 127 1 3 2011 10.1007/s10107-010-0420-4 Pegasos: Primal estimated sub-gradient solver for SVM
Expert Systems with Applications Shin 30 4 746 2006 10.1016/j.eswa.2005.07.037 Response modeling with support vector machines
Neural Computation Shin 19 3 816 2007 10.1162/neco.2007.19.3.816 Neighborhood property based pattern selection for support vector machines
Statistics and Computing Smola 14 3 199 2004 10.1023/B:STCO.0000035301.49549.88 A tutorial on support vector regression
Sun 559 2006 Proceedings of 2006 ieee international joint conference on neural networks Pattern selection for support vector regression based on sparsity and variability
Vapnik 1995 The nature of statistical learning theory
Vapnik 2006 Estimation of dependences based on empirical data
Lecture Notes in Computer Science Wang 3610 554 2005 10.1007/11539087_71 Training data selection for support vector machines
Information Sciences Wang 402 50 2017 10.1016/j.ins.2017.03.027 Remote sensing image classification based on the optimal support vector machine and modified binary coded ant colony optimization algorithm
Neurocomputing Wang 61 259 2004 10.1016/j.neucom.2003.11.012 A heuristic training for support vector regression
Applied Soft Computing Zhang 49 385 2016 10.1016/j.asoc.2016.08.026 Stock trend prediction based on new status box method and adaboost probabilistic support vector machine
해당 논문의 주제분야에서 활용도가 높은 상위 5개 콘텐츠를 보여줍니다.
더보기 버튼을 클릭하시면 더 많은 관련자료를 살펴볼 수 있습니다.
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.