莊福振

職稱：副研究員

學科類別:計算機應用

所屬部門:智能信息處理重點實驗室

專家類別:副高

主要從事機器學習、數據挖掘方面的研究，特別是在遷移學習研究以及並行數據挖掘方面，已經做出了一系列研究成果。已經在IEEE TKDE，IEEE TSMC-Part B，IJCAI，ACM CIKM，ACM WSDM等本領域頂級、重要國際期刊和國際會議上發表錄用論文40餘篇，其中遷移學習的工作獲得SAIM SDM2010以及ACM CIKM2010的最佳論文提名獎。擔任IEEE TKDE，IEEE TSMC-Part B，Information Sciences等國際期刊，計算機學報、軟件學報、自動化學報等國內學報的審稿人。獲得專利1項，申請2項，獲得軟件著作權5項。

作為項目負責人，主持國家自然科學青年基金項目1項；作為計算所方負責人，參與國家863計劃項目1項；作為科研骨幹，參與973子課題和多項國家自然科學基金項目。作為項目負責人，完成1項企業橫向項目；作為骨幹，參與完成多項企業橫向項目。

機器學習，數據挖掘，遷移學習，並行大數據挖掘

[1] Fuzhen Zhuang, Ping Luo, Zhiyong Shen, Qing He, Yuhong Xiong, Zhongzhi Shi, Hui Xiong: Mining Distinction and Commonality across Multiple Domains Using Generative Model for Text Classification. IEEE Trans. Knowl. Data Eng

[2] Fuzhen Zhuang, Ping Luo, Hui Xiong, Yuhong Xiong, Qing He, Zhongzhi Shi: Cross-Domain Learning from Multiple Sources: A Consensus Regularization Perspective. IEEE Trans. Knowl. Data Eng

[3] Fuzhen Zhuang, Ping Luo, Changying Du, Qing He, Zhongzhi Shi: Triplex transfer learning: exploiting both shared and distinct concepts for text classification. IEEE TSMC-Part B

[4] Fuzhen Zhuang, George Karypis, Xia Ning, Qing He, Zhongzhi Shi: Multi-view learning via probabilistic latent semantic analysis. Inf

[5] Fuzhen Zhuang, Ping Luo, Qing He, Zhongzhi Shi: Inductive Transfer Learning for Unlabeled Target-domain via Hybrid Regularization. Chinese Science Bulletin, Springer

[6] Fuzhen Zhuang, Ping Luo, Hui Xiong, Qing He, Yuhong Xiong, Zhongzhi Shi: Exploiting associations between word clusters and document classes for cross-domain text categorization. Statistical Analysis and Data Mining

[7] Wenjuan Luo, Fuzhen Zhuang, Qing He, Zhongzhi Shi: Exploiting relevance, coverage, and novelty for query-focused multi-document summarization. Knowl.-Based Syst

[8] Qing He, Tianfeng Shang, Fuzhen Zhuang, Zhongzhi Shi: Parallel extreme learning machine for regression based on MapReduce

[9] Xin Jin, Yongquan Liang, Dongping Tian, Fuzhen Zhuang: Particle swarm optimization using dimension selection methods. Applied Mathematics and Computation

[10] Qing He , Changying Du , Qun Wang , Fuzhen Zhuang , Zhongzhi Shi : A parallel incremental extreme SVM classifier.

[1] Fuzhen Zhuang, Ping Luo, Peifeng Yin, Qing He, Zhongzhi Shi: Concept Learning for Cross-Domain Text Classification: A General Probabilistic Framework

[2] Fuzhen Zhuang, Ping Luo, Changying Du, Qing He, Zhongzhi Shi: Triplex transfer learning: exploiting both shared and distinct concepts for text classification

[3] Fuzhen Zhuang, Ping Luo, Zhiyong Shen, Qing He, Yuhong Xiong, Zhongzhi Shi: D-LDA: A Topic Modeling Approach without Constraint Generation for Semi-defined Classification.

[4] Fuzhen Zhuang, Ping Luo, Zhiyong Shen, Qing He, Yuhong Xiong, Zhongzhi Shi, Hui Xiong: Collaborative Dual-PLSA: mining distinction and commonality across multiple domains for text classification

[5] Fuzhen Zhuang, Ping Luo, Hui Xiong, Qing He, Yuhong Xiong, Zhongzhi Shi: Exploiting Associations between Word Clusters and Document Classes for Cross-Domain Text Categorization

[6] Xin Jin, Fuzhen Zhuang, Shuhui Wang, Qing He, Zhongzhi Shi: Shared Structure Learning for Multiple Tasks with Multiple Views. ECML/PKDD

[7] Changying Du, Fuzhen Zhuang, Qing He, Zhongzhi Shi: Multi-task Semi-supervised Semantic Feature Learning for Classification

[8] Wenjuan Luo, Fuzhen Zhuang, Qing He, Zhongzhi Shi: Quad-tuple PLSA: Incorporating Entity and Its Rating in Aspect Identification

[9] Xudong Ma, Ping Luo, Fuzhen Zhuang, Qing He, Zhongzhi Shi, Zhiyong Shen: Combining Supervised and Unsupervised Models via Unconstrained Probabilistic Embedding

[10] Ping Luo, Fuzhen Zhuang, Hui Xiong, Yuhong Xiong, Qing He: Transfer learning from multiple source domains via consensus regularization.

莊福振、何清，一種採用決策樹的數據分類方法和系統，中國, 中華人民中華人民共和國國家知識產權局

敖翔、何清、莊福振. 一種基於MapReduce的分佈式垂直交叉網絡爬蟲系統,申請專利

何清，吳新宇，莊福振，敖翔，尚田豐，馬雲龍，李寧，羅文娟，韓碩，金鑫，杜長營，董智，余文超，王羣.Web數據挖掘雲服務平台

何清、馬旭東、譚慶、莊福振、趙衞中、李金成、羅文娟、李婷婷、李寧、王羣、杜長營，軟件著作權.數據挖掘雲服務平台

[1] 國家自然科學青年基金項目“基於生成模型的遷移學習算法研究及其應用”

[2] 國家863項目課題“城市人口全生命週期公共服務支撐技術研究與系統開發”

[3] 國家863項目課題“海量Web數據模式發現與結構化內容提取”

[4] 國家自然科學重點基金項目“WEB 搜索與挖掘的新理論與方法”

[5] 國家自然科學麪上基金項目“領域適應性問題相關學習算法與理論研究” ^[1]