Jain, H., Prabhu, Y. and Varma, M. (2016) Extreme Multi-Label Loss Func-tions for Recommendation, Tagging, Ranking & Other Missing Label Applications. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 935-944. https://doi.org/10.1145/2939672.2939756

has been cited by the following article:

• TITLE: Optimization Model and Algorithm for Multi-Label Learning

  AUTHORS: Zhengyang Li

  KEYWORDS: Operations Research, Multi-Label Learning, Linear Equations Solving, Optimization Algorithm

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.9 No.5, May 20, 2021

  ABSTRACT: This paper studies a kind of urban security risk assessment model based on multi-label learning, which is transformed into the solution of linear equations through a series of transformations, and then the solution of linear equations is transformed into an optimization problem. Finally, this paper uses some classical optimization algorithms to solve these optimization problems, the convergence of the algorithm is proved, and the advantages and disadvantages of several optimization methods are compared.