by So Yuen Tai. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1990. / Bibliography: leaves [144]-[146]. / ACKNOWLEDGEMENTS / TABLE OF CONTENTS --- p.T.1 / ABSTRACT / Chapter 1. --- INTRODUCTION --- p.1.1 / Chapter 1.1 --- Inexact Knowledge in Medical Expert Systems --- p.1.1 / Chapter 1.2 --- Fuzzy Expert System Shells --- p.1.2 / Chapter 1.2.1 --- SPII-2 --- p.1.3 / Chapter 1.2.2 --- Fuzzy Expert System Shell for Decision Support System --- p.1.4 / Chapter 1.3 --- Medical Expert Systems --- p.1.6 / Chapter 1.3.1 --- EXPERT --- p.1.6 / Chapter 1.3.2 --- DIABETO --- p.1.8 / Chapter 1.4 --- Impact from Micro-computer --- p.1.10 / Chapter 1.5 --- Approach --- p.1.11 / Chapter 2. --- SYSTEM Z-ll --- p.2.1 / Chapter 2.1 --- General Description --- p.2.1 / Chapter 2.2 --- Main Features --- p.2.2 / Chapter 2.2.1 --- Fuzzy Concepts --- p.2.2 / Chapter 2.2.2 --- Fuzzy Certainty --- p.2.3 / Chapter 2.2.3 --- Fuzzy Comparison --- p.2.5 / Chapter 2.2.4 --- Rule Evaluation --- p.2.7 / Chapter 2.2.5 --- Certainty Factor Propagation --- p.2.9 / Chapter 2.2.6 --- Linguistic Approximation --- p.2.10 / Chapter 2.3 --- Limitations and Possible Improvements --- p.2.11 / Chapter 3. --- A FUZZY EXPERT SYSTEM SHELL (Z-lll) IN PC ENVIRONMENT --- p.3.1 / Chapter 3.1 --- General Description --- p.3.1 / Chapter 3.2 --- Programming Environment --- p.3.1 / Chapter 3.3 --- Main Features and Structure --- p.3.3 / Chapter 3.3.1 --- Knowledge Acquisition Module --- p.3.5 / Chapter 3.3.1.1 --- Object Management Module --- p.3.5 / Chapter 3.3.1.2 --- Rule Management Module --- p.3.6 / Chapter 3.3.1.3 --- Fuzzy Term Management Module --- p.3.7 / Chapter 3.3.2 --- Consultation Module --- p.3.8 / Chapter 3.3.2.1 --- Fuzzy Inference Engine --- p.3.8 / Chapter 3.3.2.2 --- Review Management Module --- p.3.11 / Chapter 3.3.2.3 --- Linguistic Approximation Module --- p.3.11 / Chapter 3.3.3 --- System Properties Management Module --- p.3.13 / Chapter 3.4 --- Additional Features --- p.3 14 / Chapter 3.4.1 --- Weights --- p.3.15 / Chapter 3.4.1.1 --- Fuzzy Weight --- p.3.16 / Chapter 3.4.1.2 --- Fuzzy Weight Evaluation --- p.3.17 / Chapter 3.4.1.3 --- Results of Adding Fuzzy Weights --- p.3.21 / Chapter 3.4.2 --- Fuzzy Matching --- p.3.24 / Chapter 3.4.2.1 --- Similarity --- p.3.25 / Chapter 3.4.2.2 --- Evaluation of Similarity measure --- p.3.26 / Chapter 3.4.3 --- Use of System Threshold --- p.3.30 / Chapter 3.4.4 --- Use of Threshold Expression --- p.3.33 / Chapter 3.4.5 --- Playback File --- p.3.35 / Chapter 3.4.6 --- Database retrieval --- p.3.37 / Chapter 3.4.7 --- Numeric Variable Objects --- p.3.39 / Chapter 3.5 --- Implementation Highlights --- p.3.41 / Chapter 3.5.1 --- Knowledge Base --- p.4.42 / Chapter 3.5.1.1 --- Fuzzy Type --- p.4.42 / Chapter 3.5.1.2 --- Objects --- p.3.45 / Chapter 3.5.1.3 --- Rules --- p.3.49 / Chapter 3.5.2 --- System Properties --- p.3.53 / Chapter 3.5.2.1 --- System Menu --- p.3.53 / Chapter 3.5.2.2 --- Option Menu --- p.3.54 / Chapter 3.5.3 --- Consultation System --- p.3.55 / Chapter 3.5.3.1 --- Inference Engine --- p.3.56 / Chapter 3.5.3.2 --- Review Management --- p.3.60 / Chapter 3.6 --- Comparison on Z-lll and Z-ll --- p.3.61 / Chapter 3.6.1 --- Response Time --- p.3.62 / Chapter 3.6.2 --- Accessibility --- p.3.62 / Chapter 3.6.3 --- Accommodation of Large Knowledge Base --- p.3.62 / Chapter 3.6.4 --- User-Friendliness --- p.3.63 / Chapter 3.7 --- General Comments on Z-lll --- p.3.64 / Chapter 3.7.1 --- Adaptability --- p.3.64 / Chapter 3.7.2 --- Adequacy --- p.3.64 / Chapter 3.7.3 --- Applicability --- p.3.65 / Chapter 3.7.4 --- Availability --- p.3.65 / Chapter 4. --- KNOWLEDGE ENGINEERING --- p.4.1 / Chapter 4.1 --- Techniques used in Knowledge Acquisition --- p.4.1 / Chapter 4.2 --- Interviewing the Expert --- p.4.2 / Chapter 4.3 --- Knowledge Representation --- p.4.4 / Chapter 4.4 --- Development Approach --- p.4.6 / Chapter 4.5 --- Knowledge Refinement --- p.4.7 / Chapter 4.6 --- Consistency Check and Completeness Check --- p.4.12 / Chapter 4.6.1 --- The Consistency and Completeness in a nonfuzzy rule set --- p.4.13 / Chapter 4.6.1.1 --- Inconsistency in nonfuzzy rule-based system --- p.4.13 / Chapter 4.6.1.2 --- Incompleteness in nonfuzzy rule-based system --- p.4.18 / Chapter 4.6.2 --- Consistency Checks in Fuzzy Environment --- p.4.20 / Chapter 4.6.2.1 --- Affinity --- p.4.21 / Chapter 4.6.2.2 --- Detection of Inconsistency and Incompleteness in Fuzzy Environment --- p.4.24 / Chapter 4.6.3 --- Algorithm for Checking Consistency --- p.4.25 / Chapter 5. --- FUZZY MEDICAL EXPERT SYSTEMS --- p.5.1 / Chapter 5.1 --- ABVAB --- p.5.1 / Chapter 5.1.1 --- General Description --- p.5.1 / Chapter 5.1.2 --- Development of ABVAB --- p.5.2 / Chapter 5.1.3 --- Computerisation of Database --- p.5.4 / Chapter 5.1.4 --- Results of ABVAB --- p.5.7 / Chapter 5.1.5 --- From Minicomputer to PC --- p.5.15 / Chapter 5.2 --- INDUCE36 --- p.5.17 / Chapter 5.2.1 --- General Description --- p.5.17 / Chapter 5.2.2 --- Verification of INDUCE36 --- p.5.18 / Chapter 5.2.3 --- Results --- p.5.19 / Chapter 5.3 --- ESROM --- p.5.21 / Chapter 5.3.1 --- General Description --- p.5.21 / Chapter 5.3.2 --- Multi-layer Medical Expert System --- p.5.22 / Chapter 5.3.3 --- Results --- p.5.25 / Chapter 6. --- CONCLUSION --- p.6.1 / REFERENCES --- p.R.1 / APPENDIX I --- p.A.1 / APPENDIX II --- p.A.2 / APPENDIX III --- p.A.3 / APPENDIX IV --- p.A.14

by C.H. Fung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1989. / Bibliography: leaves [81-82]

by Lam Sze-sing. / "April 2000." / Thesis (Ph.D.)--Chinese University of Hong kong, 2000. / Includes bibliographical references 9p. 149-157). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

In many text mining applications, knowledge bases incorporating expert knowledge are beneficial for intelligent decision making. Refining an existing knowledge base from a source domain to a different target domain solving the same task would greatly reduce the effort required for preparing labeled training data in constructing a new knowledge base. We investigate a new framework of refining a kind of logic knowledge base known as Markov Logic Networks (MLN). One characteristic of this adaptation problem is that since the data distributions of the two domains are different, there should be different tailor-made MLNs for each domain. On the other hand, the two knowledge bases should share certain amount of similarities due to the same goal. We investigate the refinement in two situations, namely, using unlabeled target domain data, and using limited amount of labeled target domain data. / When manual annotation of a limited amount of target domain data is possible, we exploit how to actively select the data for annotation and develop two active learning approaches. The first approach is a pool-based active learning approach taking into account of the differences between the source and the target domains. A theoretical analysis on the sampling bound of the approach is conducted to demonstrate that informative data can be actively selected. The second approach is an error-driven approach that is designed to provide estimated labels for the target domain and hence the quality of the logic formulae captured for the target domain can be improved. An error analysis on the cluster-based active learning approach is presented. We have conducted extensive experiments on two different text mining tasks, namely, pronoun resolution and segmentation of citation records, showing consistent ii improvements in both situations of using unlabeled target domain data, and with a limited amount of labeled target domain data. / When there is no manual label given for the target domain data, we re-fine an existing MLN via two components. The first component is the logic formula weight adaptation that jointly maximizes the likelihood of the observations of the target domain unlabeled data and considers the differences between the two domains. Two approaches are designed to capture the differences between the two domains. One approach is to analyze the distribution divergence between the two domains and the other approach is to incorporate a penalized degree of difference. The second component is logic formula refinement where logic formulae specific to the target domain are discovered to further capture the characteristics of the target domain. / Chan, Ki Cecia. / Adviser: Wai Lam. / Source: Dissertation Abstracts International, Volume: 73-02, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 120-128). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

Typescript (photocopy). / Digitized by Kansas Correctional Industries / Department: Computer Science.

New Zealand�s main export earnings come from the primary production area including agriculture, horticulture, and viticulture. One of the major contributors in this area of horticulture is the production of quality export grade fruit; specifically apples. In order to maintain a competitive advantage, the systems and methods used to grow the fruit are constantly being refined and are increasingly based on data collected and analysed by both the orchardist who grows the produce and also researchers who refine the methods used to determine high levels of fruit quality. To support the task of data analysis and the resulting decision-making process it requires efficient and reliable tools. This thesis attempts to address this issue by applying the techniques of Connectionist-Based Intelligent Information Systems (CBIIS) for Image Analysis and Knowledge Discovery. Using advanced neurocomputing techniques and a novel knowledge engineering methodology, this thesis attempts to seek some solutions to a set of specific problems that exist within the horticultural domain. In particular it describes a methodology based on previous research into neuro-fuzzy systems for knowledge acquisition, manipulation, and extraction and furthers this area by introducing a novel and innovative knowledge-based architecture for knowledge-discovery using an on-line/real-time incremental learning system based on the Evolving Connectionist System (ECOS) paradigm known as the Evolving Fuzzy Neural Network (EFuNN). The emphases of this work highlights knowledge discovery from these data sets using a novel rule insertion and rule extraction method. The advantage of this method is that it can operate on data sets of limited sizes. This method can be used to validate the results produced by the EFuNN and also allow for greater insight into what aspects of the collected data contribute to the development of high quality produce.

Thesis (M.S.)--West Virginia University, 2004. / Title from document title page. Document formatted into pages; contains xiii, 148 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 90-95).

Thesis ( Ph.D. ) -- University of Texas at Arlington, 2008.

Thesis (M.S.)--West Virginia University, 2001. / Title from document title page. Document formatted into pages; contains xii, 121 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 118-121).

Thesis (M.S.)--Worcester Polytechnic Institute. / Keywords: run-time monitoring; continual validation; software probes; probing. Includes bibliographical references (p. 98-101).