SVM-based just-in-time adaptive classifiers

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7664 LNCS, Issue PART 2, 2012, Pages 664-672

  Alippi, Cesare ^a,b   Bu, Li ^a   Zhao, Dongbin ^a  

^a INSTITUTE OF AUTOMATION   (China)

^b POLITECNICO DI MILANO   (Italy)

Author keywords

adaptive SVM; Change Detection Tests; Concept Drifts

Indexed keywords

ADAPTIVE CLASSIFIERS; ADAPTIVE SVM; CHANGE DETECTION; CLASSIFICATION ACCURACY; CLASSIFICATION SYSTEM; CONCEPT DRIFTS; ENVIRONMENTAL CHANGE; JUST IN TIME; K-NEAREST NEIGHBORS; MEMORY REQUIREMENTS; PROCESS EVOLUTION; READOUT ELECTRONICS; STATIONARITY; TIME VARIANT; TRACK CHANGES;

DATA PROCESSING; JUST IN TIME PRODUCTION; PROBABILITY DENSITY FUNCTION;

SUPPORT VECTOR MACHINES;

EID: 84869066873     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-34481-7_81     Document Type: Conference Paper

References (11)

1. Alippi, C., Roveri, M.: Just-In-Time Adaptive Classifiers - Part I: Detecting Nonstationarity Changes. IEEE Transactions on Neural Networks 19, 1145-1153 (2008)
2. Alippi, C., Roveri, M.: Just-In-Time Adaptive Classifiers-Part II: Designing the Classifier. IEEE Transactions on Neural Networks 19, 2053-2064 (2008)
3. Alippi, C., Boracchi, G., Roveri, M.: A Just-In-Time Adaptive Classification System Based on the Intersection of Confidence Intervals Rule. Neural Networks 24, 791-800 (2011)
4. Zliobaite, I.: Learning under Concept Drift: An Overview. Technical Report, Faculty of Mathematics and Informatics (2009)
5. Elwell, R., Polikar, R.: Incremental Learning of Concept Drift in Nonstationary Environments. IEEE Transactions on Neural Networks 22, 1517-1531 (2011)
6. Oskoei, M.A., Gan, J.Q., Hu, H.S.: Adaptive Schemes Applied to Online SVM for BCI Data Classification. In: 31st Annual International Conference of the IEEE EMBS, Minneapolis, Minnesota, pp. 2600-2603 (2009)
7. Xiao, R., Wang, J.C., Sun, Z.X., Zhang, F.Y.: An Incremental SVM Learning Algorithm α-ISVM. Journal of Software 12, 1818-1824 (2001)
8. Blanzieri, E., Melgani, F.: An Adaptive SVM Nearest Neighbor Classifier for Remotely Sensed Imagery. In: Geoscience and Remote Sensing Symposium, Trento, pp. 3931-3934 (2006)
9. Bian, Z.Q., Zhang, X.G.: Pattern Recognition, pp. 285-300. Tsinghua University Press, Beijing (1999)
10. Shen, H.J., Xi, H., Xie, G.: The Improved Grid-Search Algorithm Used in the Fault Diagnosis by SVM. Mechanical Engingeering & Automation, 108-110 (2012)
11. Zeng, W.H., Ma, J.: A New Algorithm to Incremental Learning with Support Vector Machine. Journal of Xiamen University (Natural Science) 41, 687-691 (2002)