Softmax Regression Based on Bacterial Foraging Optimization Algorithm with t-Distribution Parameters

التفاصيل البيبلوغرافية
العنوان: Softmax Regression Based on Bacterial Foraging Optimization Algorithm with t-Distribution Parameters
المؤلفون: Yidan Xing, Dan Li, Wei Nai, Zan Yang, Zilin Hua, Xinyi Qiao, Yingxuan Wang
المصدر: 2021 IEEE 11th International Conference on Electronics Information and Emergency Communication (ICEIEC)2021 IEEE 11th International Conference on Electronics Information and Emergency Communication (ICEIEC).
بيانات النشر: IEEE, 2021.
سنة النشر: 2021
مصطلحات موضوعية: Computer Science::Machine Learning, Nonlinear system, Mathematical optimization, Meta learning (computer science), Computer science, Softmax function, MathematicsofComputing_NUMERICALANALYSIS, Function (mathematics), Gradient descent, Transfer of learning, Swarm intelligence, Regression
الوصف: Softmax regression is a supervised multi-class nonlinear classification algorithm in machine learning. Sometimes it is also used in regression problems. It is mainly used in transfer learning, knowledge distillation and meta learning in artificial intelligence. Softmax optimizer is gradient descent method or random gradient descent method, but its loss function is a multi peak, strong nonlinear function, and the optimal solution is only the local optimal solution rather than the global optimal solution, and depends on the gradient. So we use a swarm intelligence optimization algorithm which is independent of gradient and can find the global optimal solution to optimize. This article will introduce the bacterial foraging optimization algorithm.
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_________::c3e37916d5820aac93db7ec0ab4c4e94
https://doi.org/10.1109/iceiec51955.2021.9463821
حقوق: CLOSED
رقم الأكسشن: edsair.doi...........c3e37916d5820aac93db7ec0ab4c4e94
قاعدة البيانات: OpenAIRE