Call number: LD2668 .T4 1986 H53 / Master of Science / Electrical and Computer Engineering

by Lee Tan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1990. / Bibliography: leaves [128]-[130] / Chapter CHAPTER 1 --- INTRODUCTION --- p.1-1 / Chapter 1.1 --- Introduction to Artificial Neural Networks --- p.1-4 / Chapter 1.1.1 --- Fundamentals of Artificial Neural Networks --- p.1-5 / Chapter 1.1.2 --- Various Artificial Neural Network Models ´ؤA Review --- p.1-11 / Chapter 1.2 --- Introduction to Fuzzy Sets Theory --- p.1-17 / Chapter 1.2.1 --- "Fuzziness, Fuzzy sets and Membership Function" --- p.1-17 / Chapter 1.2.2 --- Applications of Fuzzy Sets --- p.1-19 / Connective Summary --- p.1-21 / Chapter CHAPTER 2 --- LEARNING WITH FUZZY INFORMATION --- p.2-1 / Chapter 2.1 --- "Decision Making, Pattern Associating and Pattern Classification" --- p.2-3 / Chapter 2.2 --- Artificial Neural Networks as Learning Decision Systems --- p.2-6 / Chapter 2.3 --- Fuzziness in Decision Making Processes --- p.2-10 / Chapter 2.4 --- Learning with Fuzzy Information --- p.2-12 / Chapter 2.5 --- The Formulation of Our Approach --- p.2-16 / Connective Summary --- p.2-18 / Chapter CHAPTER 3 --- A MODIFIED BACKPROPAGATION ALGORITHM FOR MULTILAYER FEEDFORWARD NETWORKS --- p.3-1 / Chapter 3.1 --- Preliminaries --- p.3-3 / Chapter 3.2 --- The Error Backpropagation Algorithm (EBPA) --- p.3-8 / Chapter 3.3 --- A Modified EBPA Learning with A Priori Fuzzy Information --- p.3-11 / Chapter 3.3.1 --- The Membership-Weighed Objective Function --- p.3-11 / Chapter 3.3.2 --- The Fuzzy Error Backpropagation Algorithm --- p.3-13 / Chapter 3.4 --- Discussion on the Proposed Fuzzy EBPA --- p.3-15 / Chapter 3.4.1 --- Methods of Determining Membership Functions --- p.3-15 / Chapter 3.4.2 --- Fuzzy EBPA Alters the Effective Target Patterns --- p.3-19 / Chapter 3.4.3 --- Estimating the Learning Rates Required for the Fuzzy EBPA --- p.3-21 / Connective Summary --- p.3-24 / Chapter CHAPTER 4 --- APPLICATION EXAMPLES --- p.4-1 / Chapter 4.1 --- A Single Node Classifier --- p.4-2 / Chapter 4.2 --- The Fuzzy XOR Problem --- p.4-29 / Chapter 4.2.1 --- Network Configuration 1 --- p.4-36 / Chapter 4.2.2 --- Network Configuration 2 --- p.4-46 / Chapter 4.2.3 --- Comments on the Simulation Results --- p.4-50 / Chapter 4.3 --- A Speech Recognition System --- p.4-54 / Connective Summary --- p.4-59 / Chapter CHAPTER 5 --- DISCUSSION AND CONCLUSION --- p.5-1

by Kuok, Chan Man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 74-77). / Abstract --- p.i / Acknowledgments --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Data Mining --- p.2 / Chapter 1.2 --- Association Rule Mining --- p.3 / Chapter 2 --- Background --- p.6 / Chapter 2.1 --- Framework of Association Rule Mining --- p.6 / Chapter 2.1.1 --- Large Itemsets --- p.6 / Chapter 2.1.2 --- Association Rules --- p.8 / Chapter 2.2 --- Association Rule Algorithms For Binary Attributes --- p.11 / Chapter 2.2.1 --- AIS --- p.12 / Chapter 2.2.2 --- SETM --- p.13 / Chapter 2.2.3 --- "Apriori, AprioriTid and AprioriHybrid" --- p.15 / Chapter 2.2.4 --- PARTITION --- p.18 / Chapter 2.3 --- Association Rule Algorithms For Numeric Attributes --- p.20 / Chapter 2.3.1 --- Quantitative Association Rules --- p.20 / Chapter 2.3.2 --- Optimized Association Rules --- p.23 / Chapter 3 --- Problem Definition --- p.25 / Chapter 3.1 --- Handling Quantitative Attributes --- p.25 / Chapter 3.1.1 --- Discrete intervals --- p.26 / Chapter 3.1.2 --- Overlapped intervals --- p.27 / Chapter 3.1.3 --- Fuzzy sets --- p.28 / Chapter 3.2 --- Fuzzy association rule --- p.31 / Chapter 3.3 --- Significance factor --- p.32 / Chapter 3.4 --- Certainty factor --- p.36 / Chapter 3.4.1 --- Using significance --- p.37 / Chapter 3.4.2 --- Using correlation --- p.38 / Chapter 3.4.3 --- Significance vs. Correlation --- p.42 / Chapter 4 --- Steps For Mining Fuzzy Association Rules --- p.43 / Chapter 4.1 --- Candidate itemsets generation --- p.44 / Chapter 4.1.1 --- Candidate 1-Itemsets --- p.45 / Chapter 4.1.2 --- Candidate k-Itemsets (k > 1) --- p.47 / Chapter 4.2 --- Large itemsets generation --- p.48 / Chapter 4.3 --- Fuzzy association rules generation --- p.49 / Chapter 5 --- Experimental Results --- p.51 / Chapter 5.1 --- Experiment One --- p.51 / Chapter 5.2 --- Experiment Two --- p.53 / Chapter 5.3 --- Experiment Three --- p.54 / Chapter 5.4 --- Experiment Four --- p.56 / Chapter 5.5 --- Experiment Five --- p.58 / Chapter 5.5.1 --- Number of Itemsets --- p.58 / Chapter 5.5.2 --- Number of Rules --- p.60 / Chapter 5.6 --- Experiment Six --- p.61 / Chapter 5.6.1 --- Varying Significance Threshold --- p.62 / Chapter 5.6.2 --- Varying Membership Threshold --- p.62 / Chapter 5.6.3 --- Varying Confidence Threshold --- p.63 / Chapter 6 --- Discussions --- p.65 / Chapter 6.1 --- User guidance --- p.65 / Chapter 6.2 --- Rule understanding --- p.67 / Chapter 6.3 --- Number of rules --- p.68 / Chapter 7 --- Conclusions and Future Works --- p.70 / Bibliography --- p.74

Abstract not available

We investigate the use of the concept of indiscernibilityand vagueness in spatial information systems. To representindiscernibility and vagueness we use rough sets, respectivelyfuzzy sets. We introduce a theoretical model to supportapproximate queries in information systems and we show howthose queries can be used to perform uncertain classi.cations.We also explore how to assess quality of uncertainclassi.cations and ways to compare those classi.cations to eachother in order to assess accuracies. We implement the querylanguage in an SQL relational language to demonstrate thefeasibility of approximate queries and we perform an experimenton real data using uncertain classi.cations.

<p>We investigate the use of the concept of indiscernibilityand vagueness in spatial information systems. To representindiscernibility and vagueness we use rough sets, respectivelyfuzzy sets. We introduce a theoretical model to supportapproximate queries in information systems and we show howthose queries can be used to perform uncertain classi.cations.We also explore how to assess quality of uncertainclassi.cations and ways to compare those classi.cations to eachother in order to assess accuracies. We implement the querylanguage in an SQL relational language to demonstrate thefeasibility of approximate queries and we perform an experimenton real data using uncertain classi.cations.</p>

Thesis (Ph. D.)--University of Hong Kong, 1989.

Poverty analysis in fisheries is dominated by assumptions of a linear relationship between fishing, income and poverty. Poverty is seen as a function of income, and income as a function of fish catch. Thus, the analytical frameworks to understand poverty in fisheries, and the policies enacted to reduce it, have focused on issues of overexploitation, regulatory mechanisms to maximize rent extraction, and technological innovation to improve fisheries’ productivity. This set of relations is underpinned by the assumption that improving fish catch per se would reduce fishers’ poverty. The study of fishing livelihoods on Lake Kossou in Côte d’Ivoire problematizes some of these assumptions. I revisit the “essentialization” of fishers with fish by utilizing the Sustainable Livelihood Approach as a lens of analysis, and by demonstrating that fishers’ livelihoods are based on a diversified portfolio of activities that span multiple sectors. Looking at livelihoods also questions the validity of the conventional “sites” of poverty analysis in fisheries (i.e. the boat, the landing site) and how these lead to misrepresentations of fishers’ livelihoods by emphasizing the upstream elements (catches) to the detriment of downstream activities in the value chain (processing and trading) that are crucial in the realization of fishers’ sustainable livelihoods. Looking at the complexity of fishers’ livelihoods sheds light on the relations between poverty (as an outcome variable) and vulnerability as a constant condition that is linked to access to multiple types of assets, the institutional contexts in which they operate, and the ways in which access to natural resources is constantly re-negotiated. To that effect, this study shows how access to Lake Kossou took a completely new meaning when the coffee-cocoa economy collapsed and young Ivorians saw it as an opportunity being stolen from them by Malian fishers. The context of post-colonial national identity formation (epitomized in the search for “Ivoirité”) served as political justification for claiming new rights to natural resources that had been relatively unimportant until then in economic terms. Finally, this study provides an innovative approach to poverty analysis by emphasizing its multiple dimensions, and by utilizing the statistical fuzzy sets methodology to construct multidimensional poverty indices.

Thesis (Ph. D.)--State University of New York at Binghamton, Department of Systems Science and Industrial Engineering, Thomas J. Watson School of Engineering and Applied Science, 2007. / Includes bibliographical references.

Thesis (Ph. D.)--West Virginia University, 1998. / Title from document title page. Document formatted into pages; contains xvii, 90 p. : ill. Vita. Includes abstract.