On-line fast kernel based methods for classification over stream data (with case studies for cyber-security)
暂无分享,去创建一个
[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..