2 papers
cs.LG2025
Learning from Double Positive and Unlabeled Data for Potential-Customer Identification
Masahiro Kato, Yuki Ikeda, Kentaro Baba +2
In this study, we propose a method for identifying potential customers in targeted marketing by applying learning from positive and unlabeled data (PU learning). We consider a scen…
cs.LG2024
Time Series Clustering with General State Space Models via Stochastic Variational Inference
Ryoichi Ishizuka, Takashi Imai, Kaoru Kawamoto
In this paper, we propose a novel method of model-based time series clustering with mixtures of general state space models (MSSMs). Each component of MSSMs is associated with each…