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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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    The Nested Indian Buffet Process :for Flexible Topic Modeling
    (2014-01-01)
    Chien, Jen-Tzung
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    Chang, Ying-Lan
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    電機工程學系
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    Department of Electrical and Computer Engineering
    This paper presents a flexible topic model based on the nested Indian buffet process (nIBP). The flexibility is achieved by relaxing three constraints: (1) number of topics is fixed, (2) topics are independent, and (3) topic hierarchy for a document is limited by a single tree path. Bayesian nonparametric learning is conducted to build a tree model where the number of topics and the topic hierarchies are automatically learnt from the given data. In particular, we propose the nIBP to construct the topic mixture model for representation of heterogeneous documents where the mixture components are flexibly selected from tree nodes or dishes that a document or customer chooses in Indian buffet process. The selection is performed in a nested and hierarchical manner. The experiments on document representation show the benefits of using the proposed nIBP.
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    Hierarchical Theme and Topic Modeling
    (2016-03)
    Chien, Jen-Tzung
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    電機學院
    ;
    College of Electrical and Computer Engineering
    Considering the hierarchical data groupings in text corpus, e.g., words, sentences, and documents, we conduct the structural learning and infer the latent themes and topics for sentences and words from a collection of documents, respectively. The relation between themes and topics under different data groupings is explored through an unsupervised procedure without limiting the number of clusters. A tree stick-breaking process is presented to draw theme proportions for different sentences. We build a hierarchical theme and topic model, which flexibly represents the heterogeneous documents using Bayesian nonparametrics. Thematic sentences and topical words are extracted. In the experiments, the proposed method is evaluated to be effective to build semantic tree structure for sentences and the corresponding words. The superiority of using tree model for selection of expressive sentences for document summarization is illustrated.
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    DIRICHLET MIXTURE ALLOCATION
    (2016)
    Chien, Jen-Tzung
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    Lee, Chao-Hsi
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    Tan, Zheng-Hua
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    電機工程學系
    ;
    Department of Electrical and Computer Engineering
    The topic model based on latent Dirichlet allocation relies on the prior statistics of topic proportionals for multinomial words. The words in a document are modeled as a random mixture of latent topics which are drawn from a single Dirichlet prior. However, a single Dirichlet distribution may not sufficiently characterize the variations of topic proportionals estimated from the heterogeneous documents. To deal with this concern, we present a Dirichlet mixture allocation (DMA) model which learns latent topics and their proportionals for topic and document clustering by using the prior based on a Dirichlet mixture model. Multiple Dirichlets pave a way to capture the structure of latent variables in learning representation from real-world documents covering a variety of topics. This paper builds a new latent variable model and develops a variational Bayesian inference procedure to learn model parameters consisting of mixture weights, Dirichlet parameters and word multinomials. Experiments on document representation show the merit of the proposed structural learning by increasing the number of Dirichlets in a DMA topic model.
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    Designing Mamdani Type Fuzzy Rule Using a Collaborative FCM Scheme
    (2013)
    Lin, Chin-Teng
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    Prasad, Mukesh
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    Chang, Jyh-Yeong
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    電子工程學系及電子研究所
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    Department of Electronics Engineering and Institute of Electronics
    This paper presents a new approach for generating fuzzy rules for fuzzy inference system by using collaborative fuzzy c-mean (CFCM). In order to do any mode of integration between datasets, there is a need to define the common feature between datasets by using some kind of collaborative process and also need to preserve the privacy and security at higher levels. This collaboration process gives a common structure between datasets which helps to define an appropriate number of rules for structural learning and also improve the accuracy of the system modeling. This all consideration bring the concept of collaborative fuzzy rule generation process with a quality measuring.