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學術出版;;Publications

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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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🏛️國立陽明交通大學機構典藏

Author: search.filters.author.Tseng, SSSubject: search.filters.subject.machine learning

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Now showing 1 - 6 of 6
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    Personal rules generation for intelligent e-mail service system
    (2000-06-01)
    Tsai, CJ
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    Tseng, SS
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    Cheng, HT
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    資訊工程學系
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    Department of Computer Science
    As E-mail becomes more popular over network, the problem of receiving a lot of undesired E-mails arise. All of the previous systems, which have been developed to solve this problem, may be divided into two different categories, client-side, and server-side, from the view of location of filter. The former case always causes waste of network traffic and the latter case results that ail users may only share a small predefined set of filtering rules. In this paper, we propose a new architecture of E-mail service system, called Intelligent E-mail Service System (IESS), integrating client-side and server-side to help users manage their E-mails. A data mining approach is used in client to find managing rules from user's behavior of reading E-mail automatically. Then these rules would be stored in rule base for predicating E-mail at server. The prediction of the E-mail is made before transferring it to client; therefore, the undesired E-mails can be filtered at server to save network traffic. In addition, the feedback mechanism is provided to refine the prediction accuracy based on each user's decisions for reading E-mails. Experimental results show that IESS can infer E-mail reading preference of individual users correctly up to the accuracy about 80%. We may conclude that IESS can help users manage their E-mails effectively.
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    A two-phase fuzzy mining and learning algorithm for adaptive learning environment
    (2001)
    Tsai, CJ
    ;
    Tseng, SS
    ;
    Lin, CY
    ;
    資訊工程學系
    ;
    Department of Computer Science
    As computer-assisted instruction environment becomes more popular over the world, the analysis of historical learning records of students becomes more important. In this work, we propose a Two-Phase Fuzzy Mining and Learning Algorithm, integrating data mining algorithm, fuzzy set theory, and look ahead mechanism, to find the embedded information, which can be provided to teachers for further analyzing, refining or reorganizing the teaching materials and tests, from historical learning records.
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    Learning concepts by arranging appropriate training order
    (2001-08-01)
    Hsu, YT
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    Hong, TP
    ;
    Tseng, SS
    ;
    資訊工程學系
    ;
    Department of Computer Science
    Machine learning has been proven useful for solving the bottlenecks in building expert systems. Noise in the training instances will, however, confuse a learning mechanism. Two main steps are adopted here to solve this problem. The first step is to appropriately arrange the training order of the instances. It is well known from Psychology that different orders of presentation of the same set of training instances to a human may cause different learning results. This idea is used here for machine learning and an order arrangement scheme is proposed. The second step is to modify a conventional noise-free learning algorithm, thus making it suitable for noisy environment. The generalized version space learning algorithm is then adopted to process the training instances for deriving good concepts. Finally, experiments on the Iris Flower problem show that the new scheme can produce a good training order, allowing the generalized version space algorithm to have a satisfactory learning result.
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    Fuzzy inductive learning strategies
    (2003-03-01)
    Wang, CH
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    Tsai, CR
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    Hong, TP
    ;
    Tseng, SS
    ;
    資訊工程學系
    In real applications, data provided to a learning system usually contain linguistic information which greatly influences concept descriptions derived by conventional inductive learning methods. Design of learning methods for working with vague data is thus very important. In this paper, we apply fuzzy set concepts to machine learning to solve this problem. A fuzzy learning algorithm based on the AQR learning strategy is proposed to manage linguistic information. The proposed learning algorithm generates fuzzy linguistic rules from "soft" instances. Experiments on the Sports and the Iris Flower classification problems are presented to compare the accuracy of the proposed algorithm with those of some other learning algorithms. Experimental results show that the rules derived from our approach are simpler and yield higher accuracy than those from some other learning algorithms.
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    Machine learning by imitating human learning
    (1996-05-01)
    Chang, KC
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    Hong, TP
    ;
    Tseng, SS
    ;
    資訊工程學系
    ;
    Department of Computer Science
    Learning general concepts in imperfect environments is difficult since training instances often include noisy data, inconclusive data, incomplete data, unknown attributes, unknown attribute values and other barriers to effective learning. It is well known that people can learn effectively in imperfect environments, and can manage to process very large amounts of data. Imitating human learning behavior therefore provides a useful model for machine learning in real-world applications. This paper proposes a new, more effective way to represent imperfect training instances and rules, and based on the new representation, a Human-Like Learning (HULL) algorithm for incrementally learning concepts well in imperfect training environments. Several examples are given to make the algorithm clearer. Finally, experimental results are presented that show the proposed learning algorithm works well in imperfect learning environments.
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    A generalized version space learning algorithm for noisy and uncertain data
    (1997-03-01)
    Hong, TP
    ;
    Tseng, SS
    ;
    資訊工程學系
    ;
    Department of Computer Science
    This paper generalizes the learning strategy of version space to manage noisy and uncertain training data. A new learning algorithm is proposed that consists of two main phases: searching and pruning. The searching phase generates and collects possible candidates into a large set; the pruning phase then prunes this set according to various criteria to find a maximally consistent version space. When the training instances cannot completely be classified, the proposed learning algorithm can make a trade-off between including positive training instances and excluding negative ones according to the requirements of different application domains. Furthermore, suitable pruning parameters are chosen according to a given time limit, so the algorithm can also make a trade-off between time complexity and accuracy. The proposed learning algorithm is then a flexible and efficient induction method that makes the version space learning strategy more practical.