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Xin Mu
Researcher at Nanjing University
Publications - 11
Citations - 422
Xin Mu is an academic researcher from Nanjing University. The author has contributed to research in topics: Supervised learning & Factoid. The author has an hindex of 8, co-authored 11 publications receiving 294 citations. Previous affiliations of Xin Mu include Singapore Management University.
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Proceedings ArticleDOI
User Identity Linkage by Latent User Space Modelling
TL;DR: This work explores a new concept of ``Latent User Space'' to more naturally model the relationship between the underlying real users and their observed projections onto the varied social platforms, such that the more similar the real users, the closer their profiles in the latent user space.
Journal ArticleDOI
Classification Under Streaming Emerging New Classes: A Solution Using Completely-Random Trees
TL;DR: In this article, completely-random trees are used as a single common core to solve all three subproblems: unsupervised learning, supervised learning, and model update on data streams.
Proceedings ArticleDOI
Cost-Effective Active Learning from Diverse Labelers
TL;DR: This paper proposes a novel active selection criterion to evaluate the cost-effectiveness of instance-labeler pairs, which ensures that the selected instance is helpful for improving the classification model, and meanwhile the selected labeler can provide an accurate label for the instance with a relative low cost.
Proceedings Article
Streaming Classification with Emerging New Class by Class Matrix Sketching
TL;DR: The proposed method dynamically maintains two low-dimensional matrix sketches to detect emerging new classes; classify known classes; and update the model in the data stream, which is superior to the existing methods.
Proceedings ArticleDOI
NeuralClassifier: An Open-source Neural Hierarchical Multi-label Text Classification Toolkit
TL;DR: NeuralClassifier is designed for quick implementation of neural models for hierarchical multi-label classification task, which is more challenging and common in real-world scenarios, including binary-class and multi-class classification.