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Softmax Model as Generalization upon Logistic Discrimination Suffers from Overfitting
並列摘要
The motivation behind this paper is to investigate the use of Softmax model for classification. We show that Softmax model is a nonlinear generalization for the logistic discrimination, that can approximate the posterior probabilities of classes where other Artificial neural network (ANN) models don't have this ability. We show that Softmax model has more flexibility than logistic discrimination in terms of correct classification. To show the performance of Softmax model a medical data set on thyroid gland state is used. The result is that Softmax model may suffer from overfitting.
被引用紀錄
Chen, Y. H. (2016). 三維自組裝完美吸收體 [master's thesis, National Taiwan University]. Airiti Library. https://doi.org/10.6342/NTU201600514
Tsai, M. H. (2008). 高熵合金與其氮化物薄膜作為銅製程擴散阻障層之研究 [doctoral dissertation, National Tsing Hua University]. Airiti Library. https://www.airitilibrary.com/Article/Detail?DocID=U0016-1410200814310110
張國強（2010）。在物件追蹤感測網路中使用資料探勘機制設
計動態分群物件追蹤演算法〔碩士論文，大同大學〕。華藝線上圖書館。https://www.airitilibrary.com/Article/Detail?DocID=U0081-3001201315110184