An Efficient Alternating Newton Method for Learning Factorization Machines

To date, factorization machines (FMs) have emerged as a powerful model in many applications. In this work, we study the training of FM with the logistic loss for binary classification, which is a nonlinear extension of the linear model with the logistic loss (i.e., logistic regression). For the trai...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	ACM transactions on intelligent systems and technology 2018-11, Vol.9 (6), p.1-31
Hauptverfasser:	Chin, Wei-Sheng, Yuan, Bo-Wen, Yang, Meng-Yuan, Lin, Chih-Jen
Format:	Artikel
Sprache:	eng
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!