<link rel="stylesheet" href="styles.cf390c6bfbe7555f.css">

學術出版;;Publications

Permanent URI for this communityhdl:11536/1

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.

Browse

Filters

1997 - 199932000 - 20011

Settings

🏛️國立陽明交通大學機構典藏

Author: search.filters.author.Hong, TPSubject: search.filters.subject.version space

Search Results

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item type:Item,
    Primal-dual version spaces for acquisition of disjunctive concepts
    (1998-06-01)
    Hong, TP
    ;
    Tseng, SS
    ;
    資訊工程學系
    ;
    Department of Computer Science
    Among incremental learning strategies, the "version space" learning strategy is one of the most well known. This learning strategy is, however, applicable only to learning conjunctive concepts. When the concepts to be learned are in disjunctive form, the version space learning strategy returns a null Version space that cannot correctly represent the desired concepts. In this paper, we present a modification of the original version space strategy that enables learning of disjunctive concepts. The new proposed version-space-based learning strategy, called the "primal-dual version-spaces" learning strategy, learns disjunctive concepts incrementally and without saving past training instances. The correctness of its underlying algorithm is analyzed and proven.
  • Thumbnail Image
    Item type:Item,
    Splitting and merging version spaces to learn disjunctive concepts
    (1999-09-01)
    Hong, TP
    ;
    Tseng, SS
    ;
    資訊工程學系
    ;
    Department of Computer Science
    We have modified the original version space strategy in order to learn disjunctive concepts incrementally and without saving past training instances. The algorithm time complexity is also analyzed, and its correctness is proven.
  • Thumbnail Image
    Item type:Item,
    Learning concepts by arranging appropriate training order
    (2001-08-01)
    Hsu, YT
    ;
    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.
  • Thumbnail Image
    Item type:Item,
    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.