The Experts below are selected from a list of 94449 Experts worldwide ranked by ideXlab platform
Kun Chang Lee - One of the best experts on this subject based on the ideXlab platform.
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investigation of online community voluntary behavior using cognitive map
Computers in Human Behavior, 2007Co-Authors: Inwon Kang, Kun Chang Lee, Sangjae Lee, Jiho ChoiAbstract:It is difficult even for experts in organizational behavior to cognitively predict the causal effect of one factor on the others. A cognitive map is used to describe the Inference Process for the investigation of online community voluntary behavior. The investigation of online community voluntary behavior demands consideration of the complex causal effect from support for member communication, perceived community value, recognition for contribution, freedom of expression, and interactive communication, to community commitment, loyalty, and social participation. A standardized causal coefficient estimated in structural equation models (SEMs) is used to create a cognitive map illustrating the effect of the status of one component on the status of another component. The cognitive map provides preliminary insights into the direction of online community voluntary behavior toward maximizing community commitment, loyalty, and social participation.
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fuzzy Inference mechanism based on fuzzy cognitive map for b2b negotiation
한국전자거래학회 학술대회 발표집, 2004Co-Authors: Kun Chang Lee, Byung Uk KangAbstract:This paper is aimed at proposing a fuzzy Inference mechanism to enhancing the quality of cognitive map-based Inference. Its main virtue lies in the two mechanisms: (1) a mechanism for avoiding a synchronization problem which is often observed during Inference Process with traditional cognitive map, and (2) a mechanism for fuzzifying decision maker's subjective judgment. Our proposed fuzzy Inference mechanism (FIM) is basically based on the cognitive map stratification algorithm which can stratify a cognitive map into number of strata and then overcome the synchronization problem successfully. Besides, the proposed FIM depends on fuzzy membership function which is administered by decision maker. With an illustrative B2B negotiation problem, we applied the proposed FIM, deducing theoretical and practical implications. Implementation was conducted by Matlab language.
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a causal knowledge driven Inference engine for expert system
Hawaii International Conference on System Sciences, 1998Co-Authors: Kun Chang Lee, Hyun Soo KimAbstract:A wide variety of knowledge acquisition methods exist for conventional knowledge types such as production rules, semantic knowledge, etc. However, the need for causal knowledge acquisition has not been stressed in the expert systems field. The objectives of this paper are to: suggest a causal knowledge acquisition Process; and investigate the causal knowledge-based Inference Process. FCM (Fuzzy Cognitive Map), a fuzzy signed digraph with causal relationships between concept variables found in a specific application domain, is used for the causal knowledge acquisition. Although FCM has plenty of generic properties for causal knowledge acquisition, it needs some theoretical improvement for acquiring more refined causal knowledge. In this sense, we refine fuzzy implications of FCM by proposing a fuzzy causal relationship and fuzzy partially causal relationship. To test the validity of our proposed approach, we prototype a causal knowledge-driven Inference engine named CAKES and then experiment with illustrative examples.
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a causal knowledge driven Inference engine for expert system
Journal of The Korean Institute of Intelligent Systems, 1998Co-Authors: Kun Chang Lee, Hyun Soo KimAbstract:Although many methods of knowledge acquisition has been developed in the exper systems field, such a need form causal knowledge acquisition hs not been stressed relatively. In this respect, this paper is aimed at suggesting a causal knowledge acquisition Process, and then investigate the causal knowledge-based Inference Process. A vehicle for causal knowledge acquisition is FCM (Fuzzy Cognitive Map), a fuzzy signed digraph with causal relationships between concept variables found in a specific application domain. Although FCM has a plenty of generic properties for causal knowledge acquisition, it needs some theoretical improvement for acquiring a more refined causal knowledge. In this sense, we refine fuzzy implications of FCM by proposing fuzzy causal relationship and fuzzy partially causal relationship. To test the validity of our proposed approach, we prototyped a causal knowledge-driven Inference engine named CAKES and then experimented with some illustrative examples.
Robert Krovetz - One of the best experts on this subject based on the ideXlab platform.
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Viewing morphology as an Inference Process
Artificial Intelligence, 2000Co-Authors: Robert KrovetzAbstract:Abstract Morphology is the area of linguistics concerned with the internal structure of words. Information retrieval has generally not paid much attention to word structure, other than to account for some of the variability in word forms via the use of stemmers. We report on our experiments to determine the importance of morphology, and the effect that it has on performance. We found that grouping morphological variants makes a significant improvement in retrieval performance. Improvements are seen by grouping inflectional as well as derivational variants. We also found that performance was enhanced by recognizing lexical phrases. We describe the interaction between morphology and lexical ambiguity, and how resolving that ambiguity will lead to further improvements in performance.
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viewing morphology as an Inference Process
International ACM SIGIR Conference on Research and Development in Information Retrieval, 1993Co-Authors: Robert KrovetzAbstract:Morphology is the area of linguistics concerned with the internal structure of words. Information Retrieval has generally not paid much attention to word structure, other than to account for some of the variability in word forms via the use of stemmers. This paper will describe our experiments to determine the importance of morphology, and the effect that it has on performance. We will also describe the role of morphological analysis in word sense disambiguation, and in identifying lexical semantic relationships in a machine-readable dictionary. We will first provide a brief overview of morphological phenomena, and then describe the experiments themselves.
Hyun Soo Kim - One of the best experts on this subject based on the ideXlab platform.
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a causal knowledge driven Inference engine for expert system
Hawaii International Conference on System Sciences, 1998Co-Authors: Kun Chang Lee, Hyun Soo KimAbstract:A wide variety of knowledge acquisition methods exist for conventional knowledge types such as production rules, semantic knowledge, etc. However, the need for causal knowledge acquisition has not been stressed in the expert systems field. The objectives of this paper are to: suggest a causal knowledge acquisition Process; and investigate the causal knowledge-based Inference Process. FCM (Fuzzy Cognitive Map), a fuzzy signed digraph with causal relationships between concept variables found in a specific application domain, is used for the causal knowledge acquisition. Although FCM has plenty of generic properties for causal knowledge acquisition, it needs some theoretical improvement for acquiring more refined causal knowledge. In this sense, we refine fuzzy implications of FCM by proposing a fuzzy causal relationship and fuzzy partially causal relationship. To test the validity of our proposed approach, we prototype a causal knowledge-driven Inference engine named CAKES and then experiment with illustrative examples.
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a causal knowledge driven Inference engine for expert system
Journal of The Korean Institute of Intelligent Systems, 1998Co-Authors: Kun Chang Lee, Hyun Soo KimAbstract:Although many methods of knowledge acquisition has been developed in the exper systems field, such a need form causal knowledge acquisition hs not been stressed relatively. In this respect, this paper is aimed at suggesting a causal knowledge acquisition Process, and then investigate the causal knowledge-based Inference Process. A vehicle for causal knowledge acquisition is FCM (Fuzzy Cognitive Map), a fuzzy signed digraph with causal relationships between concept variables found in a specific application domain. Although FCM has a plenty of generic properties for causal knowledge acquisition, it needs some theoretical improvement for acquiring a more refined causal knowledge. In this sense, we refine fuzzy implications of FCM by proposing fuzzy causal relationship and fuzzy partially causal relationship. To test the validity of our proposed approach, we prototyped a causal knowledge-driven Inference engine named CAKES and then experimented with some illustrative examples.
Tongseng Quah - One of the best experts on this subject based on the ideXlab platform.
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a graphical operating environment for neural network expert systems
International Joint Conference on Neural Network, 1991Co-Authors: Tongseng QuahAbstract:A window-based platform, known as the Graphical Environment for Neuronet Expert Systems (GENES), is proposed. The platform provides the user with an easy-to-learn, easy-to-use operating environment for creating, training, editing, and enhancing neural-network-based expert systems. The underlying neural logic network (NELONET) has been shown to be capable of doing logical inferencing and is used in two large-scale-operation expert systems. Building on top of the X-window system and the OPENLOOK user interface, GENES inherits the select-and-perform operation strategy for neural network objects. The system's knowledge base contains simple network elements that correspond to rules in a conventional system. During the Inference Process, these network elements are linked up dynamically to form a large neural network which will operate according to the NELONET activation rules. >
Juan F Huete - One of the best experts on this subject based on the ideXlab platform.
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A Theoretical Framework for Web Categorization in Hierarchical Directories using Bayesian Networks
2013Co-Authors: Luis M De Campos, Juan M. Fernández-luna, Juan F HueteAbstract:Summary. In this paper, we shall present a theoretical framework for classifying web pages in a hierarchical directory using the Bayesian Network formalism. In particular, we shall focus on the problem of multi-label text categorization, where a given document can be assigned to any number of categories in the hierarchy. The idea is to explicitly represent the dependence relationships between the different categories in the hierarchy, although adapted to include the category descriptors. Given a new document (web page) to be classified, a Bayesian Network Inference Process shall be used to compute the probability of each category given the document. The web page is then assigned to those classes with the highest posterior probability.
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the bnr model foundations and performance of a bayesian network based retrieval model
International Journal of Approximate Reasoning, 2003Co-Authors: Luis M De Campos, Juan M Fernandezluna, Juan F HueteAbstract:This paper presents an information retrieval model based on the Bayesian network formalism. The topology of the network (representing the dependence relationships between terms and documents) as well as the quantitative knowledge (the probabilities encoding the strength of these relationships) will be mined from the document collection using automatic learning algorithms. The relevance of a document to a given query is obtained by means of an Inference Process through a complex network of dependences. A new Inference technique, called propagation + evaluation, has been developed in order to obtain the exact probabilities of relevance in the whole network efficiently.
