Granular support vector machine: a review

[1]  Wenjian Wang,et al.  Granular support vector machine: a review , 2017, Artificial Intelligence Review.

[2]  Yan Shi,et al.  Monitoring and assessing fruit freshness in IOT-based e-commerce delivery using scenario analysis and interval number approaches , 2016, Inf. Sci..

[3]  Wenjian Wang,et al.  Support vector machine based on hierarchical and dynamical granulation , 2016, Neurocomputing.

[4]  Christopher Leckie,et al.  High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning , 2016, Pattern Recognit..

[5]  Pierpaolo D'Urso,et al.  Fuzzy c-ordered medoids clustering for interval-valued data , 2016, Pattern Recognit..

[6]  Dan Wang,et al.  Granular data imputation: A framework of Granular Computing , 2016, Appl. Soft Comput..

[7]  Yan Shi,et al.  Optimizing the intermodal transportation of emergency medical supplies using balanced fuzzy clustering , 2016 .

[8]  Richard F. Hartl,et al.  Supply chain dynamics, control and disruption management , 2016 .

[9]  Wenjian Wang,et al.  An active learning-based SVM multi-class classification model , 2015, Pattern Recognit..

[10]  Shifei Ding,et al.  Research on the hybrid models of granular computing and support vector machine , 2015, Artificial Intelligence Review.

[11]  Divya Tomar,et al.  Twin Support Vector Machine: A review from 2007 to 2014 , 2015 .

[12]  Zhiquan Qi,et al.  Review on: Twin Support Vector Machines , 2014, Annals of Data Science.

[13]  Wenjian Wang,et al.  Saliency-SVM: An automatic approach for image segmentation , 2014, Neurocomputing.

[14]  Hu-Sheng Guo,et al.  Dynamical Granular Support Vector Regression Machine: Dynamical Granular Support Vector Regression Machine , 2014 .

[15]  Yan Shi,et al.  Developing fast predictors for large-scale time series using fuzzy granular support vector machines , 2013, Appl. Soft Comput..

[16]  Wang Wen-jian,et al.  Granular support vector machine based on mixed measure , 2013 .

[17]  Ching Y. Suen,et al.  A novel hybrid CNN-SVM classifier for recognizing handwritten digits , 2012, Pattern Recognit..

[18]  Yu Cao,et al.  Predicting Financial Distress of Chinese Listed Companies Using Rough Set Theory and Support Vector Machine , 2011, Asia Pac. J. Oper. Res..

[19]  Avi Ma'ayan,et al.  SVM classifier to predict genes important for self-renewal and pluripotency of mouse embryonic stem cells , 2010, BMC Systems Biology.

[20]  Madan Gopal,et al.  A hybrid SVM based decision tree , 2010, Pattern Recognit..

[21]  Ping-Feng Pai,et al.  A fuzzy support vector regression model for business cycle predictions , 2010, Expert Syst. Appl..

[22]  Yu Hongyi,et al.  Probability density estimation based on SVM , 2009, 2009 Global Mobile Congress.

[23]  Matthew Johnson,et al.  Protein local 3D structure prediction by Super Granule Support Vector Machines (Super GSVM) , 2009, BMC Bioinformatics.

[24]  Morten Nielsen,et al.  NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction , 2009, BMC Bioinformatics.

[25]  Hu-Sheng Guo,et al.  A novel learning model-Kernel Granular Support Vector Machine , 2009, 2009 International Conference on Machine Learning and Cybernetics.

[26]  Chia-Hui Huang,et al.  Interval Regression Analysis with Soft-Margin Reduced Support Vector Machine , 2009, IEA/AIE.

[27]  Yanqing Zhang,et al.  SVMs Modeling for Highly Imbalanced Classification , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[28]  Shaoning Pang,et al.  r-SVMT: Discovering the knowledge of association rule over SVM classification trees , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[29]  Andrzej Bargiela,et al.  Toward a Theory of Granular Computing for Human-Centered Information Processing , 2008, IEEE Transactions on Fuzzy Systems.

[30]  Pankaj Kumar,et al.  Granular Support Vector Machine Based Method for Prediction of Solubility of Proteins on Overexpression in Escherichia Coli , 2007, PReMI.

[31]  Jiang-She Zhang,et al.  Reducing examples to accelerate support vector regression , 2007, Pattern Recognit. Lett..

[32]  Jingtao Yao.  A Ten-year Review of Granular Computing , 2007, 2007 IEEE International Conference on Granular Computing (GRC 2007).

[33]  Yanqing Zhang,et al.  A New Improved K-Means Algorithm with Penalized Term , 2007, 2007 IEEE International Conference on Granular Computing (GRC 2007).

[34]  Madan Gopal,et al.  SVM-Based Tree-Type Neural Networks as a Critic in Adaptive Critic Designs for Control , 2007, IEEE Transactions on Neural Networks.

[35]  M. Narasimha Murty,et al.  Multiclass core vector machine , 2007, ICML '07.

[36]  Yanqing Zhang,et al.  Fast and Effective Spam Sender Detection with Granular SVM on Highly Imbalanced Mail Server Behavior Data , 2006, 2006 International Conference on Collaborative Computing: Networking, Applications and Worksharing.

[37]  S. Abe,et al.  Incremental training of support vector machines using hyperspheres , 2006, Pattern Recognit. Lett..

[38]  Geoffrey J. Gordon,et al.  The support vector decomposition machine , 2006, ICML.

[39]  Chengjun Liu,et al.  Face detection using discriminating feature analysis and Support Vector Machine , 2006, Pattern Recognit..

[40]  Ivor W. Tsang,et al.  Core Vector Machines: Fast SVM Training on Very Large Data Sets , 2005, J. Mach. Learn. Res..

[41]  Jiawei Han,et al.  Making SVMs Scalable to Large Data Sets using Hierarchical Cluster Indexing , 2005, Data Mining and Knowledge Discovery.

[42]  Yanqing Zhang,et al.  Granular support vector machines with association rules mining for protein homology prediction , 2005, Artif. Intell. Medicine.

[43]  Yanqing Zhang,et al.  Granular support vector machines for medical binary classification problems , 2004, 2004 Symposium on Computational Intelligence in Bioinformatics and Computational Biology.

[44]  Xuegong Zhang,et al.  Using class-center vectors to build support vector machines , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

[45]  Vladimir Cherkassky,et al.  The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.

[46]  Federico Girosi,et al.  Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[47]  Laura Schweitzer,et al.  Advances In Kernel Methods Support Vector Learning , 2016 .

[48]  Wenjian Wang,et al.  Granular support vector machine based on mixed measure , 2013, Neurocomputing.

[49]  Guo Hu,et al.  Dynamical Granular Support Vector Regression Machine , 2013 .

[50]  Zhang Yan-ping,et al.  Research of yield prediction model based on support vector machine within the framework of quotient space theory. , 2009 .

[51]  Wenjian Wang,et al.  Online prediction model based on support vector machine , 2008, Neurocomputing.

[52]  John C. Platt,et al.  Fast Training of Support Vector Machines using Sequential Minimal Optimization , 2000 .

[53]  Narendra Ahuja,et al.  A geometric approach to train support vector machines , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[54]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[55]  Thorsten Joachims,et al.  Making large scale SVM learning practical , 1998 .