On-line fast kernel based methods for classification over stream data (with case studies for cyber-security)

[1]  Shaoning Pang,et al.  Inductive vs transductive inference, global vs local models: SVM, TSVM, and SVMT for gene expression classification problems , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[2]  Shaoning Pang,et al.  Factorizing Class Characteristics via Group MEBs Construction , 2010, ICONIP.

[3]  George Hripcsak,et al.  Technical Brief: Agreement, the F-Measure, and Reliability in Information Retrieval , 2005, J. Am. Medical Informatics Assoc..

[4]  Heikki Mannila,et al.  Principles of Data Mining , 2001, Undergraduate Topics in Computer Science.

[5]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[6]  Geoff Hulten,et al.  Mining high-speed data streams , 2000, KDD '00.

[7]  Eamonn J. Keogh,et al.  A symbolic representation of time series, with implications for streaming algorithms , 2003, DMKD '03.

[8]  Heikki Mannila,et al.  Time series segmentation for context recognition in mobile devices , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[9]  Nikola K. Kasabov,et al.  DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction , 2002, IEEE Trans. Fuzzy Syst..

[10]  Nikola K. Kasabov,et al.  ECOS: Evolving Connectionist Systems and the ECO Learning Paradigm , 1998, ICONIP.

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

[12]  N. Oki,et al.  A radial basis function network (RBFN) for function approximation , 1999, 42nd Midwest Symposium on Circuits and Systems (Cat. No.99CH36356).

[13]  Rajeev Motwani,et al.  Maintaining variance and k-medians over data stream windows , 2003, PODS.

[14]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[15]  Philip S. Yu,et al.  A Framework for Projected Clustering of High Dimensional Data Streams , 2004, VLDB.

[16]  Thorsten Joachims,et al.  Transductive Learning via Spectral Graph Partitioning , 2003, ICML.

[17]  David J. Hand,et al.  Statistics and data mining: intersecting disciplines , 1999, SKDD.

[18]  Shaoning Pang,et al.  Hierarchical Core Vector Machines for Network Intrusion Detection , 2009, ICONIP.

[19]  S. Henikoff,et al.  Amino acid substitution matrices from protein blocks. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[20]  Nikola Kasabov,et al.  Evolving Connectionist Systems: The Knowledge Engineering Approach , 2007 .

[21]  Nikola K. Kasabov,et al.  Evolving fuzzy neural networks for supervised/unsupervised online knowledge-based learning , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[22]  Ashish Goel,et al.  Reductions among high dimensional proximity problems , 2001, SODA '01.

[23]  Qiang Chen,et al.  An anomaly detection technique based on a chi‐square statistic for detecting intrusions into information systems , 2001 .

[24]  Daijin Kim,et al.  Face recognition using the embedded HMM with second-order block-specific observations , 2003, Pattern Recognit..

[25]  Mohammad Alshayeb,et al.  EVE: On-Board Process Planning and Execution , 2002 .

[26]  Usama M. Fayyad,et al.  Mining Databases: Towards Algorithms for Knowledge Discovery , 1998, IEEE Data Eng. Bull..

[27]  Ricardo Vilalta,et al.  Metalearning - Applications to Data Mining , 2008, Cognitive Technologies.

[28]  Guoping Wang,et al.  Learning with progressive transductive support vector machine , 2003, Pattern Recognit. Lett..

[29]  Gunnar Rätsch,et al.  Large Scale Multiple Kernel Learning , 2006, J. Mach. Learn. Res..

[30]  Shaoning Pang,et al.  Two-Class SVM Trees (2-SVMT) for Biomarker Data Analysis , 2006, ISNN.

[31]  Yiming Yang,et al.  RCV1: A New Benchmark Collection for Text Categorization Research , 2004, J. Mach. Learn. Res..

[32]  Marie-France Sagot,et al.  Spelling Approximate Repeated or Common Motifs Using a Suffix Tree , 1998, LATIN.

[33]  Keinosuke Fukunaga,et al.  Introduction to statistical pattern recognition (2nd ed.) , 1990 .

[34]  Philip S. Yu,et al.  Mining concept-drifting data streams using ensemble classifiers , 2003, KDD '03.

[35]  S. Muthukrishnan,et al.  Data streams: algorithms and applications , 2005, SODA '03.

[36]  Lukasz Golab,et al.  Issues in data stream management , 2003, SGMD.

[37]  Xin Yao,et al.  Bagging and Boosting Negatively Correlated Neural Networks , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[38]  N. Megiddo Linear-time algorithms for linear programming in R3 and related problems , 1982, FOCS 1982.

[39]  Eamonn J. Keogh,et al.  Clustering of time-series subsequences is meaningless: implications for previous and future research , 2004, Knowledge and Information Systems.

[40]  Sheng-Hsun Hsu,et al.  Application of SVM and ANN for intrusion detection , 2005, Comput. Oper. Res..

[41]  Carlos Soares,et al.  A Meta-Learning Method to Select the Kernel Width in Support Vector Regression , 2004, Machine Learning.

[42]  Stuart E. Schechter,et al.  Anonymous Authentication of Membership in Dynamic Groups , 1999, Financial Cryptography.

[43]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[44]  Rajeev Motwani,et al.  Load Shedding Techniques for Data Stream Systems , 2003 .

[45]  Charless C. Fowlkes,et al.  Diamond Eye: a distributed architecture for image data mining , 1999, Defense, Security, and Sensing.

[46]  Hussein A. Abbass,et al.  Analysis of CCME: Coevolutionary Dynamics, Automatic Problem Decomposition, and Regularization , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

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

[48]  Thorsten Joachims,et al.  Transductive Inference for Text Classification using Support Vector Machines , 1999, ICML.

[49]  Carlos Soares,et al.  Ranking Learning Algorithms: Using IBL and Meta-Learning on Accuracy and Time Results , 2003, Machine Learning.

[50]  Hong Qiao,et al.  An online core vector machine with adaptive MEB adjustment , 2010, Pattern Recognit..

[51]  Piotr Indyk,et al.  Approximate clustering via core-sets , 2002, STOC '02.

[52]  Guangyou Xu,et al.  A Face Verification Algorithm Integrating Geometrical and Template Features , 2001, IEEE Pacific Rim Conference on Multimedia.

[53]  Piotr Indyk,et al.  Identifying Representative Trends in Massive Time Series Data Sets Using Sketches , 2000, VLDB.

[54]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[55]  Plamen P. Angelov,et al.  Automatic generation of fuzzy rule-based models from data by genetic algorithms , 2003, Inf. Sci..

[56]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[57]  Nikola K. Kasabov,et al.  Brain-, Gene-, and Quantum Inspired Computational Intelligence: Challenges and Opportunities , 2007, Challenges for Computational Intelligence.

[58]  Nello Cristianini,et al.  Kernel Methods for Exploratory Pattern Analysis: A Demonstration on Text Data , 2004, SSPR/SPR.

[59]  Yoram Singer,et al.  BoosTexter: A Boosting-based System for Text Categorization , 2000, Machine Learning.

[60]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[61]  Christos Faloutsos,et al.  Adaptive, Hands-Off Stream Mining , 2003, VLDB.

[62]  Wolfgang Nejdl,et al.  MailRank: using ranking for spam detection , 2005, CIKM '05.

[63]  Tom Fawcett,et al.  ROC Graphs: Notes and Practical Considerations for Data Mining Researchers , 2003 .

[64]  Nikola Kasabov,et al.  Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines , 2002, IEEE Transactions on Neural Networks.

[65]  Graham Cormode,et al.  What's hot and what's not: tracking most frequent items dynamically , 2003, TODS.

[66]  Dennis Shasha,et al.  StatStream: Statistical Monitoring of Thousands of Data Streams in Real Time , 2002, VLDB.

[67]  Hava T. Siegelmann,et al.  Support Vector Clustering , 2002, J. Mach. Learn. Res..

[68]  Gholamreza Haffari,et al.  Transductive learning for statistical machine translation , 2007, ACL.

[69]  Plamen P. Angelov,et al.  An approach for fuzzy rule-base adaptation using on-line clustering , 2004, Int. J. Approx. Reason..