최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기Neural networks : the official journal of the International Neural Network Society, v.53, 2014년, pp.95 - 108
Lapin, M. , Hein, M. , Schiele, B.
Prior knowledge can be used to improve predictive performance of learning algorithms or reduce the amount of data required for training. The same goal is pursued within the learning using privileged information paradigm which was recently introduced by Vapnik et al. and is aimed at utilizing additio...
Optimization Methods & Software Bennett 1 1 23 1992 10.1080/10556789208805504 Robust linear programming discrimination of two linearly inseparable sets
Borwein 2000 Convex analysis and nonlinear optimization: theory and examples
Boyd 2004 Convex optimization
Burges, C.J.C., & Crisp, D.J. (1999). Uniqueness of the SVM solution. In NIPS (pp. 223-229).
ACM Transactions on Intelligent Systems and Technology Chang 2 27:1 2011 10.1145/1961189.1961199 LIBSVM: a library for support vector machines
Neural Computation Chapelle 19 5 1155 2007 10.1162/neco.2007.19.5.1155 Training a support vector machine in the primal
2006 Semi-supervised learning
Chen, J., Liu, X., & Lyu, S. (2012). Boosting with side information. In ACCV (pp. 5-9).
Cortes, C., Mansour, Y., & Mohri, M. (2010). Learning bounds for importance weighting. In NIPS (pp. 442-450).
Elkan, C. (2001). The foundations of cost-sensitive learning. In IJCAI (pp. 973-978).
Medical Physics Elter 34 11 4164 2007 10.1118/1.2786864 The prediction of breast cancer biopsy outcomes using two CAD approaches that both emphasize an intelligible decision process
Information Sciences Feyereisl 194 4 2012 10.1016/j.ins.2011.04.025 Privileged information for data clustering
10.1007/978-3-642-33266-1_40 Fouad, S., Tino, P., Raychaudhury, S., & Schneider, P. (2012). Learning using privileged information in prototype based models. In ICANN (pp. 322-329).
Frank, A., & Asuncion, A. (2010). UCI machine learning repository. http://archive.ics.uci.edu/ml.
Econometrica: Journal of the Econometric Society Heckman 153 1979 10.2307/1912352 Sample selection bias as a specification error
Neurocomputing Lauer 71 7-9 1578 2008 10.1016/j.neucom.2007.04.010 Incorporating prior knowledge in support vector machines for classification: a review
Neural Networks Liang 22 5-6 766 2009 10.1016/j.neunet.2009.06.030 Predictive learning with structured (grouped) data
Liang, L., & Cherkassky, V. (2008). Connection between SVM+ and multi-task learning. In IJCNN (pp. 2048-2054).
IEEE Transactions on Neural Networks Lin 13 2 464 2002 10.1109/72.991432 Fuzzy support vector machines
10.1007/3-540-36755-1_23 Margineantu, D.D. (2002). Class probability estimation and cost-sensitive classification decisions. In ECML (pp. 270-281).
Nocedal 2006 Numerical optimization
Pechyony, D., & Vapnik, V. (2010). On the theory of learnining with privileged information. In NIPS (pp. 1894-1902).
Pechyony 2011 Statistical learning and data science Fast optimization algorithms for solving SVM+
10.1007/3-540-44581-1_27 Scholkopf, B., Herbrich, R., & Smola, A. (2001). A generalized representer theorem. In COLT (pp. 416-426).
Scholkopf, B., Simard, P.Y., Smola, A.J., & Vapnik, V.N. (1998). Prior knowledge in support vector kernels. In NIPS (pp. 640-646).
Scholkopf 2002 Learning with kernels: support vector machines, regularization, optimization and beyond
Journal of Statistical Planning and Inference Shimodaira 90 2 227 2000 10.1016/S0378-3758(00)00115-4 Improving predictive inference under covariate shift by weighting the log-likelihood function
Statistics & Decisions Sugiyama 23 4 249 2005 Input-dependent estimation of generalization error under covariate shift
Vapnik 2006 Empirical inference science afterword of 2006
Neural Networks Vapnik 22 5-6 544 2009 10.1016/j.neunet.2009.06.042 A new learning paradigm: learning using privileged information
Yang, X., Song, Q., & Cao, A. (2005). Weighted support vector machine for data classification. In IJCNN (pp. 859-864).
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
저자가 공개 리포지터리에 출판본, post-print, 또는 pre-print를 셀프 아카이빙 하여 자유로운 이용이 가능한 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.