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Yuan Yan Tang
Researcher at University of Macau
Publications - 674
Citations - 15632
Yuan Yan Tang is an academic researcher from University of Macau. The author has contributed to research in topics: Wavelet & Wavelet transform. The author has an hindex of 58, co-authored 647 publications receiving 12835 citations. Previous affiliations of Yuan Yan Tang include Hong Kong Community College & Southwest Baptist University.
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Journal ArticleDOI
A cost-sensitive meta-learning classifier: SPFCNN-Miner
TL;DR: This paper proposes the Siamese Parallel Fully-connected Neural Network as a binary classifier and uses the meta learning method to deal with the problem of class-unbalanced data distribution and effective results show that the performance of the proposed approach is better than that of the comparison methods.
Journal ArticleDOI
A combined Weisfeiler–Lehman graph kernel for structured data
TL;DR: A common method to construct combined graph kernel (CGK) which is based on a family of graph kernels, including a highly efficient subtree kernel, edge kernel, and shortest path kernel is developed.
Journal ArticleDOI
NNMap: A method to construct a good embedding for nearest neighbor classification
Jing Chen,Jing Chen,Yuan Yan Tang,Yuan Yan Tang,C. L. Philip Chen,Bin Fang,Zhaowei Shang,Yuewei Lin +7 more
TL;DR: A quantitative criterion of embedding quality assessment for nearest neighbor classification is defined, and a method called NNMap is presented to construct a good embedding to deal with the practical shortages of nearest neighbor classifier.
Proceedings ArticleDOI
Location and recognition of legal amounts on Chinese bank cheques
TL;DR: A technique using each currency unit as a key word to locate/extract the legal amount in bank cheques using a Chinese cheque processing system under development at the Centre for Pattern Recognition and Machine Intelligence.
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A diversified shared latent variable model for efficient image characteristics extraction and modelling
TL;DR: The Diversified Shared Latent Variable Model (DSLVM) is proposed to extract and manipulate object attributes in an unsupervised way and can accurately deal with various object.