Cascaded SVMs in Pattern Classification for Time-Sensitive Separating

This paper introduced some fundamental background knowledge of Support Vector Machine, including VC dimension, separable hyperplane, and feature space as well. Two kinds of cascaded SVMs architecture are reviewed, i.e. parallel and serial structure Parallel SVMs can reduce the run time effectively. And serial SVMs is being used for multi-class separating, and discard the negative samples at the early stage. In the end we proposed two potential applications using cascaded SVMs.

[1]  Nello Cristianini,et al.  Support vector machine classification and validation of cancer tissue samples using microarray expression data , 2000, Bioinform..

[2]  Jing Yang.  An Improved Cascade SVM Training Algorithm with Crossed Feedbacks , 2006, First International Multi-Symposiums on Computer and Computational Sciences (IMSCCS'06).

[3]  Ioannis Pitas,et al.  Facial Expression Recognition in Videos using a Novel Multi-Class Support Vector Machines Variant , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[4]  B. Schölkopf,et al.  Efficient face detection by a cascaded support–vector machine expansion , 2004, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[5]  R. Brereton,et al.  Support vector machines for classification and regression. , 2010, The Analyst.

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

[8]  Brendan McCane,et al.  Red Blood Cell Classification through Depth Map and Surface Feature , 2008, 2008 International Symposium on Computer Science and Computational Technology.

[9]  V. Vapnik.  Estimation of Dependences Based on Empirical Data , 2006 .

[10]  Junchul Chun,et al.  Classification of Facial Expression Using SVM for Emotion Care Service System , 2008, 2008 Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing.

[11]  Jing Yang,et al.  A parallel SVM training algorithm on large-scale classification problems , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[12]  Martin Brown,et al.  Network Performance Assessment for Neurofuzzy Data Modelling , 1997, IDA.

[13]  Gavin C. Cawley,et al.  Efficient leave-one-out cross-validation of kernel fisher discriminant classifiers , 2003, Pattern Recognit..

[14]  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.

[15]  Lifeng Sun,et al.  A cascade SVM approach for head-shoulder detection using histograms of oriented gradients , 2009, 2009 IEEE International Symposium on Circuits and Systems.

[16]  D.K. Iakovidis,et al.  A cascading support vector machines system for gene expression data classification , 2004, 2004 2nd International IEEE Conference on 'Intelligent Systems'. Proceedings (IEEE Cat. No.04EX791).

[17]  Xiaoqing Ding,et al.  Face Detection Based on Cost-Sensitive Support Vector Machines , 2002, SVM.

[18]  D Haussler,et al.  Knowledge-based analysis of microarray gene expression data by using support vector machines. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[19]  Ioannis Pitas,et al.  Real time facial expression recognition from image sequences using support vector machines , 2005, IEEE International Conference on Image Processing 2005.