Institution
Hong Kong Baptist University
Education•Hong Kong, China•
About: Hong Kong Baptist University is a education organization based out in Hong Kong, China. It is known for research contribution in the topics: China & Population. The organization has 7811 authors who have published 18919 publications receiving 555274 citations. The organization is also known as: Hong Kong Baptist College & HKBU.
Topics: China, Population, Catalysis, Context (language use), Computer science
Papers published on a yearly basis
Papers
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TL;DR: This paper proposes a k-means-type algorithm that is able to provide data clustering and outlier detection simultaneously by incorporating an additional cluster into the objective function and designs an iterative procedure to optimize the objectivefunction of the proposed algorithm and establish the convergence of the Iterative procedure.
167 citations
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TL;DR: This paper proposes a novel solution, called Trasductive Multilabel Classification (TraM), to effectively assign a set of multiple labels to each instance, and formulate the transductive multilabel learning as an optimization problem of estimating label concept compositions.
Abstract: The problem of multilabel classification has attracted great interest in the last decade, where each instance can be assigned with a set of multiple class labels simultaneously. It has a wide variety of real-world applications, e.g., automatic image annotations and gene function analysis. Current research on multilabel classification focuses on supervised settings which assume existence of large amounts of labeled training data. However, in many applications, the labeling of multilabeled data is extremely expensive and time consuming, while there are often abundant unlabeled data available. In this paper, we study the problem of transductive multilabel learning and propose a novel solution, called Trasductive Multilabel Classification (TraM), to effectively assign a set of multiple labels to each instance. Different from supervised multilabel learning methods, we estimate the label sets of the unlabeled instances effectively by utilizing the information from both labeled and unlabeled data. We first formulate the transductive multilabel learning as an optimization problem of estimating label concept compositions. Then, we derive a closed-form solution to this optimization problem and propose an effective algorithm to assign label sets to the unlabeled instances. Empirical studies on several real-world multilabel learning tasks demonstrate that our TraM method can effectively boost the performance of multilabel classification by using both labeled and unlabeled data.
167 citations
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TL;DR: Optimal choices of the inner iteration steps in the IHSS( CG, Lanczos) and IH SS(CG, CGNE) iterations are discussed in detail by considering both global convergence speed and overall computation workload, and computational efficiencies of both inexact iterations are analyzed and compared deliberately.
167 citations
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TL;DR: Tolerant populations of these species would serve as potential candidates for re-vegetation of wastelands contaminated with Pb, Zn and Cu and suggested that co-tolerant ecotypes have evolved in the two grasses.
167 citations
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TL;DR: The results presented that HCH and 5 Stockholm Convention pesticides were detected in Hong Kong soils although the detectable ratio varies to a great extent and close correlations of pH(KCl) and total organic carbon (TOC) with HCH indicated an effect on the residues of HCH caused by these two soils properties, but such relationships were not found with DDT or other OCPs.
167 citations
Authors
Showing all 7946 results
Name | H-index | Papers | Citations |
---|---|---|---|
Weihong Tan | 140 | 892 | 67151 |
Bin Liu | 138 | 2181 | 87085 |
Jun Lu | 135 | 1526 | 99767 |
John P. Giesy | 114 | 1162 | 62790 |
Qiang Yang | 112 | 1117 | 71540 |
Ming Hung Wong | 103 | 710 | 39738 |
Wei Wang | 95 | 3544 | 59660 |
Jianhua Zhang | 92 | 415 | 28085 |
Xiaojun Wu | 91 | 1088 | 31687 |
Guibin Jiang | 88 | 850 | 34633 |
Shu Tao | 87 | 639 | 27304 |
Paul K.S. Lam | 87 | 485 | 25614 |
Cheng-Yong Su | 87 | 581 | 32322 |
Hai-Long Jiang | 86 | 198 | 30946 |
Baowen Li | 83 | 477 | 23080 |