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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.Hong, TP

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Now showing 1 - 10 of 24
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    A new probabilistic induction method
    (1997-02-01)
    Hou, RH
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    Hong, TP
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    Tseng, SS
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    Kuo, SY
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    資訊工程學系
    Knowledge acquisition by interviewing a domain expert is one of the most problematic aspects of the development of expert systems. As an alternative, methods for inducing concept descriptions from examples have proven useful in eliminating this bottleneck. In this paper, we propose a probabilistic induction method (PIM), which is an improvement of the Chan and Wong method, for detecting relevant patterns implicit in a given data set. PIM uses the technique of residual analysis and several heuristics to effectively detect complex relevant patterns and to avoid the problem of combinatorial explosion. A reasonable trade-off between the induction time and the classification ratio is achieved. Moreover, PIM quickly classifies unknown objects using classification rules converted from the positively relevant patterns detected. Three experiments are conducted to confirm the validity of PIM.
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    A new hybrid learning algorithm for non-linear boundaries
    (1998-06-01)
    Wang, CH
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    Hong, TP
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    Tseng, SS
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    資訊工程學系
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    Department of Computer Science
    In this paper, we propose a new hybrid learning algorithm, ETNC, which incorporates the popular decision-tree learning algorithm ASSISTANT into a modified three-layer back-propagation learning method to construct an entropy-tree net classifier. The new Iearning algorithm also adopts a tree-pruning mechanism to avoid overfitting problems. The new algorithm decreases both the tree size and error rate, especially for complex classification problems. Furthermore, it is not necessary for users to lay out the structure of a tree net in advance; instead, the structure is automatically constructed in the tree-growing process. Finally, the results of experiments in diagnosing brain tumors and classifying sugar canes are described to compare the proposed algorithm with two other learning methods, the back-propagation learning algorithm and ASSISTANT, in terms of accuracy, knowledge complexity and learning speed. Experimental results show that the proposed learning algorithm can provide a good trade-off between accuracy, knowledge complexity and learning speed.
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    Primal-dual version spaces for acquisition of disjunctive concepts
    (1998-06-01)
    Hong, TP
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    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.
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    Automatically integrating multiple rule sets in a distributed-knowledge environment
    (1998-08-01)
    Wang, CH
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    Hong, TP
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    Tseng, SS
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    Liao, CM
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    資訊工程學系
    In this paper, an actual knowledge application is made by means of evolution paradigms in terms of knowledge acquisition. rin automatic knowledge integration approach in a distributed-knowledge environment is thus proposed to integrate multiple rule sets into a single effective rule set. The proposed approach consists of two phases: knowledge encoding and knowledge integration. In the encoding phase, each knowledge input is translated and expressed as a rule set, then encoded as a bit string. The combined bit strings from multiple knowledge inputs form an initial knowledge population, which is then ready for integration. In the knowledge integration phase, a genetic search technique generates an optimal or nearly optimal rule set from these initial knowledge-input strings. Finally, experimental results from diagnosis of brain tumors show that the rule set derived by the proposed approach is much more accurate than each initial rule set.
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    A fuzzy inductive learning strategy for modular rules
    (1999-04-01)
    Wang, CH
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    Liu, JF
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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. The design of learning methods to learn concept descriptions in 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 maximum information gain is proposed to manage linguistic information. The proposed learning algorithm generates fuzzy rules from "soft" instances, which differ from conventional instances in that they have class membership values. 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. (C) 1999 Elsevier Science B.V. All rights reserved.
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    Splitting and merging version spaces to learn disjunctive concepts
    (1999-09-01)
    Hong, TP
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    Tseng, SS
    ;
    資訊工程學系
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    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.
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    Integrating membership functions and fuzzy rule sets from multiple knowledge sources
    (2000-05-16)
    Wang, CH
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    Hong, TP
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    Tseng, SS
    ;
    資訊工程學系
    ;
    Department of Computer Science
    Ln this paper, we propose a GA-based fuzzy knowledge-integration framework that can simultaneously integrate multiple fuzzy rule sets and their membership function sets. The proposed two-phase approach includes fuzzy knowledge encoding and fuzzy knowledge integration. In the encoding phase, each fuzzy rule set with its associated membership functions is first transformed into an intermediary representation, and further encoded as a string. The combined strings form an initial knowledge population which is then ready for integration. In the knowledge-integration phase. a genetic algorithm is used to generate an optimal or nearly optimal set of fuzzy rules and membership functions from the initial knowledge population. The hepatitis diagnostic problem was used to show the performance of the proposed knowledge-integration approach. Results show that the fuzzy knowledge-base resulting from using our approach performs better than every individual knowledge base. (C) 2000 Elsevier Science B.V. All rights reserved.
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    A coverage-based genetic knowledge-integration strategy
    (2000-07-01)
    Wang, CH
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    Hong, TP
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    Chang, MB
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    Tseng, SS
    ;
    資訊工程學系
    In this paper, we propose a coverage-based genetic knowledge-integration approach to effectively integrate multiple rule sets into a centralized knowledge base. The proposed approach consists of two phases: knowledge encoding and knowledge integration. In the knowledge-encoding phase, each rule in the various rule sets that are derived from different sources (such as expert knowledge or existing knowledge bases) is first translated and encoded as a fixed-length bit string. The bit strings combined together thus form an initial knowledge population. In the knowledge-integration phase, a genetic algorithm applies genetic operations and credit assignment at each rule-string to generate an optimal or nearly optimal rule set. Experiments on diagnosing brain tumors were made to compare the accuracy of a rule set generated by the proposed approach with that of the initial rule sets derived from different groups of experts or induced by various machine learning techniques. Results show that the rule set derived by the proposed approach is more accurate than each initial rule set on its own. (C) 2000 Elsevier Science Ltd. All rights reserved.
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    A genetics-based approach to knowledge integration and refinement
    (2001-01-01)
    Wang, CH
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    Hong, TP
    ;
    Tseng, SS
    ;
    資訊工程學系
    ;
    Department of Computer Science
    In this paper, we propose a genetics-based knowledge integration approach to integrate multiple rule sets into a central rule set. The proposed approach consists of two phases: knowledge encoding and knowledge integrating. In the encoding phase, each knowledge input is translated and expressed as a rule set, and then encoded as a bit string. The combined bit strings form an initial knowledge population, which is then ready for integrating. In the knowledge integration phase, a genetic algorithm generates an optimal or nearly optimal rule set from these initial knowledge inputs. Furthermore, a rule-refinement scheme is proposed to refine inference rules via interaction with the environment. Experiments on diagnosing brain tumors were carried out to compare the accuracy of a rule set generated by the proposed approach with that of initial rule sets derived from different groups of experts or induced by means of various machine learning techniques. Results show that the rule set derived using the proposed approach is much more accurate than each initial rule set on its own.
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    Learning concepts by arranging appropriate training order
    (2001-08-01)
    Hsu, YT
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    Hong, TP
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    Tseng, SS
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    資訊工程學系
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    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.