Chan Chi Hang. / Thesis submitted in: August 2004. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 105-114). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Problem Statement --- p.3 / Chapter 1.2 --- Major Contributions --- p.5 / Chapter 1.3 --- Publication List --- p.6 / Chapter 1.4 --- Thesis Organization --- p.7 / Chapter 2 --- Background Survey --- p.9 / Chapter 2.1 --- Relevance Feedback Framework --- p.9 / Chapter 2.1.1 --- Relevance Feedback Types --- p.11 / Chapter 2.1.2 --- Data Distribution --- p.12 / Chapter 2.1.3 --- Training Set Size --- p.14 / Chapter 2.1.4 --- Inter-Query Learning and Intra-Query Learning --- p.15 / Chapter 2.2 --- History of Relevance Feedback Techniques --- p.16 / Chapter 2.3 --- Relevance Feedback Approaches --- p.19 / Chapter 2.3.1 --- Vector Space Model --- p.19 / Chapter 2.3.2 --- Ad-hoc Re-weighting --- p.26 / Chapter 2.3.3 --- Distance Optimization Approach --- p.29 / Chapter 2.3.4 --- Probabilistic Model --- p.33 / Chapter 2.3.5 --- Bayesian Approach --- p.39 / Chapter 2.3.6 --- Density Estimation Approach --- p.42 / Chapter 2.3.7 --- Support Vector Machine --- p.48 / Chapter 2.4 --- Presentation Set Selection --- p.52 / Chapter 2.4.1 --- Most-probable strategy --- p.52 / Chapter 2.4.2 --- Most-informative strategy --- p.52 / Chapter 3 --- Biased Support Vector Machine for Content-Based Image Retrieval --- p.57 / Chapter 3.1 --- Motivation --- p.57 / Chapter 3.2 --- Background --- p.58 / Chapter 3.2.1 --- Regular Support Vector Machine --- p.59 / Chapter 3.2.2 --- One-class Support Vector Machine --- p.61 / Chapter 3.3 --- Biased Support Vector Machine --- p.63 / Chapter 3.4 --- Interpretation of parameters in BSVM --- p.67 / Chapter 3.5 --- Soft Label Biased Support Vector Machine --- p.69 / Chapter 3.6 --- Interpretation of parameters in Soft Label BSVM --- p.73 / Chapter 3.7 --- Relevance Feedback Using Biased Support Vector Machine --- p.74 / Chapter 3.7.1 --- Advantages of BSVM in Relevance Feedback . . --- p.74 / Chapter 3.7.2 --- Relevance Feedback Algorithm By BSVM --- p.75 / Chapter 3.8 --- Experiments --- p.78 / Chapter 3.8.1 --- Synthetic Dataset --- p.80 / Chapter 3.8.2 --- Real-World Dataset --- p.81 / Chapter 3.8.3 --- Experimental Results --- p.83 / Chapter 3.9 --- Conclusion --- p.86 / Chapter 4 --- Self-Organizing Map-based Inter-Query Learning --- p.88 / Chapter 4.1 --- Motivation --- p.88 / Chapter 4.2 --- Algorithm --- p.89 / Chapter 4.2.1 --- Initialization and Replication of SOM --- p.89 / Chapter 4.2.2 --- SOM Training for Inter-Query Learning --- p.90 / Chapter 4.2.3 --- Incorporate with Intra-Query Learning --- p.92 / Chapter 4.3 --- Experiments --- p.93 / Chapter 4.3.1 --- Synthetic Dataset --- p.95 / Chapter 4.3.2 --- Real-World Dataset --- p.95 / Chapter 4.3.3 --- Experimental Results --- p.97 / Chapter 4.4 --- Conclusion --- p.98 / Chapter 5 --- Conclusion --- p.102 / Bibliography --- p.104
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_325242 |
| Date | January 2005 |
| Contributors | Chan, Chi Hang., Chinese University of Hong Kong Graduate School. Division of Computer Science and Engineering. |
| Source Sets | The Chinese University of Hong Kong |
| Language | English, Chinese |
| Detected Language | English |
| Type | Text, bibliography |
| Format | print, ix, 114 leaves : ill. ; 30 cm. |
| Rights | Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
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