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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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PPD: A practical parallel loop detector for parallelizing compilers on multiprocessor systems
(1996-11-01)
Yang, CT
;
Wu, CT
;
Tseng, SS
;
資訊工程學系
;
Department of Computer Science
It is well known that extracting parallel loops plays a significant role in designing parallelizing compilers. The execution efficiency of a loop is enhanced when the loop can be executed in parallel or partial parallel, like a DOALL or DOACROSS loop. This paper reports on the practical parallelism detector (PPD) that is implemented in PFPC (a portable FORTRAN parallelizing compiler running on OSF/1) at NCTU to concentrate on finding the parallelism available in loops. The PPD can extract the potential DOALL and DOACROSS loops in a program by invoking a combination of the ZIV test and the I test for verifying array subscripts. Furthermore, if DOACROSS loops are available, an optimization of synchronization statement is made. Experimental results show that PPD is more reliable and accurate than previous approaches.
A new probabilistic induction method
(1997-02-01)
Hou, RH
;
Hong, TP
;
Tseng, SS
;
Kuo, SY
;
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.
A new hybrid learning algorithm for non-linear boundaries
(1998-06-01)
Wang, CH
;
Hong, TP
;
Tseng, SS
;
資訊工程學系
;
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.
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.
Automatically integrating multiple rule sets in a distributed-knowledge environment
(1998-08-01)
Wang, CH
;
Hong, TP
;
Tseng, SS
;
Liao, CM
;
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.
A fuzzy inductive learning strategy for modular rules
(1999-04-01)
Wang, CH
;
Liu, JF
;
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.
Intelligent query agent for structural document databases
(1999-08-01)
Jiang, MF
;
Tseng, SS
;
Tsai, CJ
;
資訊工程學系
;
Department of Computer Science
Querying a database for document retrieval is often a process close to querying an answering expert system. In this work, we apply the expert system techniques to the intelligent query agent establishment and regard the structural document database as the expertise which can be the objective of the knowledge acquisition. A new knowledge representation, named Structural Documents (SDs), is proposed to be the base of our model, and a transformation process from the raw data to the format of a database is applied. Based on the SDs, more suitable results could be inferenced rapidly by inference engine, and the flow of inference is also described. For implementation, an intelligent Chinese information retrieval system for personnel regulations by integrating knowledge-based and full-text searching techniques is proposed. In our experiments, the structural information of the documents can be acquired from the database using the knowledge extraction module. By observing the operating process of users, we found the query process of users are simplified. (C) 1999 Published by Elsevier Science Ltd. All rights reserved.
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.
Genetic algorithm for extended cell assignment problem in wireless ATM network
(2000)
Din, DR
;
Tseng, SS
;
Jiang, MF
;
資訊工程學系
;
Department of Computer Science
In this paper, we investigate the extended cell assignment problem which optimally assigns new and split cells in PCS (Personal Communication Service) to switches in a wireless ATM network. Given cells and switches in an ATM network (whose locations are fixed and known), the problem is assigning cells to switches in an optimum manner. We would like to do the assignment in as attempt to minimize a cost criterion. The cost has two components: one is the cost of handoffs that involve two switches, and the other is the cost of cabling. This problem is modeled as a complex integer programming problem and finding an optimal solution to this problem is NP-complete. A stochastic search method, based on a genetic approach is proposed to solve this problem. Simulation results show that genetic algorithm is robust for this problem.
An intelligent parallel loop scheduling for parallelizing compilers
(2000-03-01)
Fann, YW
;
Yang, CT
;
Tseng, SS
;
Tsai, CJ
;
資訊工程學系
In this paper we propose a knowledge-based approach to solving loop-scheduling problems. A rule-based system, called IPLS, is developed by combining a repertory grid and an attribute ordering table to construct a knowledge base. IPLS chooses an appropriate scheduling algorithm by inferring some features of loops and assigning parallel loops to multiprocessors to achieve significant speedup. Because more attributes are proposed, the accuracy of selection of an appropriate scheduling method is improved. In addition, the refined IPLS system can automatically adjust the attributes in the knowledge base according to profile information; therefore, IPLS has the capability of feedback learning. The experimental results show that our approach can achieve greater speedup on multiprocessor systems than can others.

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