Class-prior estimation for learning from positive and unlabeled data-Reference-Cited by-同舟云学术

Class-prior estimation for learning from positive and unlabeled data

Published:2016-11-14 Issue:4 Volume:106 Page:463-492
ISSN:0885-6125
Container-title:Machine Learning
language:en
Short-container-title:Mach Learn

Author:

du Plessis Marthinus C.,Niu Gang,Sugiyama Masashi

Funder

Ministry of Education, Culture, Sports, Science, and Technology

Japan Science and Technology Agency

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/content/pdf/10.1007/s10994-016-5604-6.pdf

Reference21 articles.

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3. Bonnans, F., & Cominetti, R. (1996). Perturbed optimization in Banach spaces I: A general theory based on a weak directional constraint qualification; II: A theory based on a strong directional qualification condition; III: Semiinfinite optimization. SIAM Journal on Control and Optimization, 34, 1151–1171, 1172–1189, 1555–1567.

4. Bonnans, F., & Shapiro, A. (1998). Optimization problems with perturbations, a guided tour. SIAM Review, 40(2), 228–264.

5. Csiszár, I. (1967). Information-type measures of difference of probability distributions and indirect observation. Studia Scientiarum Mathematicarum Hungarica, 2, 229–318.

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