Zhi‐Hua Zhou

Multi-Label Learning by Exploiting Label Correlations Locally

2021 · Proceedings of the AAAI Conference on Artificial Intelligence

It is well known that exploiting label correlations is important for multi-label learning. Existing approaches typically exploit label correlations globally, by assuming that the label correlations are shared by all the instances. In real-world tasks, …

Multi-label Learning with Emerging New Labels

2016

Multi-label learning is widely applied in many tasks, where an object possesses multiple concepts with each represented by a class label. Previous studies on multi-label learning have focused on a fixed set of class labels, …

Distributed Deep Forest and its Application to Automatic Detection of Cash-out Fraud

2018 · arXiv (Cornell University)

Internet companies are facing the need for handling large-scale machine learning applications on a daily basis and distributed implementation of machine learning algorithms which can handle extra-large scale tasks with great performance is widely needed. …

Learning to Generate Posters of Scientific Papers by Probabilistic Graphical Models

2017 · arXiv (Cornell University)

Researchers often summarize their work in the form of scientific posters. Posters provide a coherent and efficient way to convey core ideas expressed in scientific papers. Generating a good scientific poster, however, is a complex …