Scaling up support vector machines with application to plankton recognition
作者： Tong Luo
DOI:
关键词: Support vector machine 、 Artificial intelligence 、 Scaling 、 Machine learning 、 Active learning (machine learning) 、 Computer science
摘要:
参考文章(87)
David Cohn, Greg Schohn, Less is More: Active Learning with Support Vector Machines international conference on machine learning. pp. 839- 846 ,(2000)
Simon Tong, Daphne Koller, Support Vector Machine Active Learning with Application sto Text Classification international conference on machine learning. pp. 999- 1006 ,(2000)
Vladimir Naumovich Vapnik, Estimation of Dependences Based on Empirical Data ,(2010)
G. Rätsch, T. Onoda, K.-R. Müller, Soft Margins for AdaBoost Machine Learning. ,vol. 42, pp. 287- 320 ,(2001) , 10.1023/A:1007618119488
Xiaoou Tang, W. Kenneth Stewart, He Huang, Scott M. Gallager, Cabell S. Davis, Luc Vincent, Marty Marra, Automatic Plankton Image Recognition Artificial Intelligence Review. ,vol. 12, pp. 177- 199 ,(1998) , 10.1023/A:1006517211724
Mozer, B Schölkopf, M.J. Jordan, Cjc Burges, T. Petsche, Improving the accuracy and speed of support vector learning machines neural information processing systems. pp. 375- 381 ,(1997)
Marc G. Genton, Classes of kernels for machine learning: a statistics perspective international conference on artificial intelligence and statistics. ,vol. 2, pp. 299- 312 ,(2002) , 10.5555/944790.944815
Thorsten Joachims, Making large-scale support vector machine learning practical Advances in kernel methods. pp. 169- 184 ,(1999)
Richard A Olshen, Charles J Stone, Leo Breiman, Jerome H Friedman, Classification and regression trees ,(1983)
Thomas G. Dietterich, An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization Machine Learning. ,vol. 40, pp. 139- 157 ,(2000) , 10.1023/A:1007607513941