Typescript (photocopy). / Digitized by Kansas Correctional Industries

Typescript (photocopy). / Digitized by Kansas Correctional Industries

Thesis. 1978. M.S.--Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / M.S.

Thesis (M.S.)--Massachusetts Institute of Technology, Alfred P. Sloan School of Management, 1980. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND DEWEY. / Includes bibliographical references. / by Lawrence Abram Krakauer. / M.S.

Thesis (M.S.V.S.)--Massachusetts Institute of Technology, Dept. of Architecture, 1981. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ROTCH. / Includes bibliographies. / Sculpture as process is rooted in the historical development of movement as a theme of art in general and of sculpture in particular since 1900. The impact of the industrial revolution and the subsequent scientific/technological boom moved sculpture increasingly from static to dynamic models of reality. Scientific research pushed beyond the natural limitations of the senses, expanding man's perception of reality and demanding an ever more encompassing world view. That which was previously unseen, unheard and unknown became tangible in the micro and macro perspectives of the "New Landscape" (Kepes). Change arrived with challenge - to integrate the "New Landscape" with the familiar - and accompanied by turbulent social transformation. Whether rejected or embraced, the machine became an obsessive metaphor for both human progress and destruction. As a synthesis of object and process, it catalyzed the transition from static to dynamic models of reality. The initially rough 'machine aesthetic' led to the development of kinetic sculpture and towards the integration of art, audience and environment. With the introduction of electronics and the computer, movement became less fascinating as an isolated phenomenon by gaining meaning as an integral part of a whole system. Cybernetic mechanisms - regulatory functions controlling input and output of organic and inorganic systems - became important aspects of new perception and models. Processes of communication within systems and between systems came to define a dynamic scale, inversely related, of parts to the whole. Sculpture as process, the term my thesis seeks to define and my installation to embody, generates these communication processes in the environment, materializes and records them as temporary dynamic patterns, and stores them as information in a randomly accessible memory. / by Bernd Kracke. / M.S.V.S.

程序員常利用現有的應用程序介面建立新程序，但遇到的困難是花很多時間去找尋并學習適當的應用程序介面 [8]。在這篇論文中，我們提出新方向幫助對某應用程序介面比較陌生的程序員：以短語搜尋應用程序介面連通子圖。我們以一大圖表達應用程序介面的調用，當用家提交一組文字短語后，便能從這大圖中找出一個符合需要的最理想連通子圖。 / 這新方向的挑戰是簡單的子圖搜尋需要很大的搜尋空間。我們提出兩組機制改良了一套現有的貪婪子圖搜尋算法，以此找出一個其節點與搜尋短語的文字相近的連通子圖。另外，這套現有的貪婪子圖搜尋算法需要很短的圖節點之間的最短路徑計算時間，我們提出了一套空間效率高的索引，能較快的找出節點間的精確最短路徑。從實驗中，我們通過兩組現實生活中的搜尋數據比較了此新方法與一最新式的程序碼建議方法Portfolio [19]，發現兩組數據的平均F₁-Measure能分別有效地提高了64%與36%。 / Reusing APIs of existing libraries is a common practice during software development, but searching suitable APIs and their usages can be time-consuming [8]. There have been studies to help users nd usages of APIs given names of functions. In this paper, we study a new and more practical approach to help users nd usages of APIs given only simple text phrases expressed in natural language, when users have limited knowledge about an API library. We model API invocations as an API graph and aim to nd an optimum connected subgraph that meets users’ search needs. / The problem is challenging since the search space in an API graph is very huge. We start with a greedy subgraph search algorithm which returns a connected subgraph containing nodes with high textual similarity to the query phrases. Two renement techniques are proposed to improve the quality of the returned subgraph. Furthermore, as the greedy subgraph search algorithm relies on online query of shortest path between two graph nodes, we propose a space-effcient compressed shortest path indexing scheme that can eciently recover the exact shortest path. We conduct extensive experiments to show that the proposed subgraph search approach for API recommendation is very eective in that it boosts the average F₁-measure of the state-of-the-art approach, Portfolio [19], on two groups of real-life queries by 64% and 36% respectively. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Chan, Wing Kwan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 59-62). / Abstracts also in Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation & Challenges --- p.2 / Chapter 1.2 --- Contributions --- p.4 / Chapter 1.3 --- Organization of Thesis --- p.5 / Chapter 2 --- Related Works --- p.7 / Chapter 2.1 --- Keyword Search in Graphs --- p.7 / Chapter 2.2 --- Team Formation in Expert Network --- p.8 / Chapter 2.3 --- API/Code Recommendation --- p.10 / Chapter 3 --- Problem Statement & Proposed Approach --- p.12 / Chapter 3.1 --- Problem Statement --- p.12 / Chapter 3.2 --- API Subgraph Search --- p.15 / Chapter 3.2.1 --- A Greedy Subgraph Search Algorithm --- p.16 / Chapter 3.2.2 --- Selecting Node with High Textual Similarity --- p.19 / Chapter 3.2.3 --- Handling Multiple Shortest Paths Problem --- p.21 / Chapter 3.2.4 --- Time Complexity --- p.24 / Chapter 3.2.5 --- Approximation Ratio --- p.25 / Chapter 3.3 --- Class-Only Path Indexing --- p.25 / Chapter 3.3.1 --- Three Indexing Structures --- p.27 / Chapter 3.3.2 --- Exact Path Recovery --- p.28 / Chapter 3.3.3 --- Space Complexity --- p.30 / Chapter 3.4 --- Alternative Approaches --- p.31 / Chapter 3.4.1 --- Enhanced Steiner Tree --- p.31 / Chapter 3.4.2 --- Finding R-clique --- p.32 / Chapter 4 --- Experiments --- p.35 / Chapter 4.1 --- Effectiveness - Among Subgraph Searching Algorithms --- p.35 / Chapter 4.1.1 --- Dataset --- p.35 / Chapter 4.1.2 --- Results --- p.36 / Chapter 4.2 --- Effectiveness - Between Two API Recommendations --- p.38 / Chapter 4.2.1 --- Query Formulation --- p.39 / Chapter 4.2.2 --- Results --- p.41 / Chapter 4.3 --- Effciency - Runtime --- p.44 / Chapter 4.4 --- Indexing Comparison Class Graph Vs. Full Graph --- p.46 / Chapter 4.4.1 --- Runtime & Memory --- p.46 / Chapter 4.4.2 --- Gain Score --- p.48 / Chapter 4.5 --- A Comparison with Finding R-Clique --- p.49 / Chapter 4.6 --- Threats to Validity --- p.50 / Chapter 5 --- Conclusion & Future Works --- p.55 / Chapter 5.1 --- Conclusion --- p.55 / Chapter 5.2 --- Future Works --- p.56 / Bibliography --- p.59

Thesis. 1978. M.Arch.A.S.--Massachusetts Institute of Technology. Dept. of Architecture. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ROTCH. / Bibliography: leaves 100-103. / M.Arch.A.S.

Lee Kit Ying. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 115-117). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Project Background --- p.1 / Chapter 1.2 --- Problem Specifications --- p.3 / Chapter 1.3 --- Contributions --- p.5 / Chapter 1.4 --- Thesis Organization --- p.6 / Chapter 2 --- Background --- p.8 / Chapter 2.1 --- Medical Data Mining --- p.8 / Chapter 2.1.1 --- General Information --- p.9 / Chapter 2.1.2 --- Related Research --- p.10 / Chapter 2.1.3 --- Characteristics and Difficulties Encountered --- p.11 / Chapter 2.2 --- DNA Sequence Analysis --- p.13 / Chapter 2.3 --- Hepatitis B Virus --- p.14 / Chapter 2.3.1 --- Virus Characteristics --- p.15 / Chapter 2.3.2 --- Important Findings on the Virus --- p.17 / Chapter 2.4 --- Bayesian Network and its Classifiers --- p.17 / Chapter 2.4.1 --- Formal Definition --- p.18 / Chapter 2.4.2 --- Existing Learning Algorithms --- p.19 / Chapter 2.4.3 --- Evolutionary Algorithms and Hybrid EP (HEP) --- p.22 / Chapter 2.4.4 --- Bayesian Network Classifiers --- p.25 / Chapter 2.4.5 --- Learning Algorithms for BN Classifiers --- p.32 / Chapter 3 --- Bayesian Network Classifier for Clinical Data --- p.35 / Chapter 3.1 --- Related Work --- p.36 / Chapter 3.2 --- Proposed BN-augmented Naive Bayes Classifier (BAN) --- p.38 / Chapter 3.2.1 --- Definition --- p.38 / Chapter 3.2.2 --- Learning Algorithm with HEP --- p.39 / Chapter 3.2.3 --- Modifications on HEP --- p.39 / Chapter 3.3 --- Proposed General Bayesian Network with Markov Blan- ket (GBN) --- p.40 / Chapter 3.3.1 --- Definition --- p.41 / Chapter 3.3.2 --- Learning Algorithm with HEP --- p.41 / Chapter 3.4 --- Findings on Bayesian Network Parameters Calculation --- p.43 / Chapter 3.4.1 --- Situation and Errors --- p.43 / Chapter 3.4.2 --- Proposed Solution --- p.46 / Chapter 3.5 --- Performance Analysis on Proposed BN Classifier Learn- ing Algorithms --- p.47 / Chapter 3.5.1 --- Experimental Methodology --- p.47 / Chapter 3.5.2 --- Benchmark Data --- p.48 / Chapter 3.5.3 --- Clinical Data --- p.50 / Chapter 3.5.4 --- Discussion --- p.55 / Chapter 3.6 --- Summary --- p.56 / Chapter 4 --- Classification in DNA Analysis --- p.57 / Chapter 4.1 --- Related Work --- p.58 / Chapter 4.2 --- Problem Definition --- p.59 / Chapter 4.3 --- Proposed Methodology Architecture --- p.60 / Chapter 4.3.1 --- Overall Design --- p.60 / Chapter 4.3.2 --- Important Components --- p.62 / Chapter 4.4 --- Clustering --- p.63 / Chapter 4.5 --- Feature Selection Algorithms --- p.65 / Chapter 4.5.1 --- Information Gain --- p.66 / Chapter 4.5.2 --- Other Approaches --- p.67 / Chapter 4.6 --- Classification Algorithms --- p.67 / Chapter 4.6.1 --- Naive Bayes Classifier --- p.68 / Chapter 4.6.2 --- Decision Tree --- p.68 / Chapter 4.6.3 --- Neural Networks --- p.68 / Chapter 4.6.4 --- Other Approaches --- p.69 / Chapter 4.7 --- Important Points on Evaluation --- p.69 / Chapter 4.7.1 --- Errors --- p.70 / Chapter 4.7.2 --- Independent Test --- p.70 / Chapter 4.8 --- Performance Analysis on Classification of DNA Data --- p.71 / Chapter 4.8.1 --- Experimental Methodology --- p.71 / Chapter 4.8.2 --- Using Naive-Bayes Classifier --- p.73 / Chapter 4.8.3 --- Using Decision Tree --- p.73 / Chapter 4.8.4 --- Using Neural Network --- p.74 / Chapter 4.8.5 --- Discussion --- p.76 / Chapter 4.9 --- Summary --- p.77 / Chapter 5 --- Adaptive HEP for Learning Bayesian Network Struc- ture --- p.78 / Chapter 5.1 --- Background --- p.79 / Chapter 5.1.1 --- Objective --- p.79 / Chapter 5.1.2 --- Related Work - AEGA --- p.79 / Chapter 5.2 --- Feasibility Study --- p.80 / Chapter 5.3 --- Proposed A-HEP Algorithm --- p.82 / Chapter 5.3.1 --- Structural Dissimilarity Comparison --- p.82 / Chapter 5.3.2 --- Dynamic Population Size --- p.83 / Chapter 5.4 --- Evaluation on Proposed Algorithm --- p.88 / Chapter 5.4.1 --- Experimental Methodology --- p.89 / Chapter 5.4.2 --- Comparison on Running Time --- p.93 / Chapter 5.4.3 --- Comparison on Fitness of Final Network --- p.94 / Chapter 5.4.4 --- Comparison on Similarity to the Original Network --- p.95 / Chapter 5.4.5 --- Parameter Study --- p.96 / Chapter 5.5 --- Applications on Medical Domain --- p.100 / Chapter 5.5.1 --- Discussion --- p.100 / Chapter 5.5.2 --- An Example --- p.101 / Chapter 5.6 --- Summary --- p.105 / Chapter 6 --- Conclusion --- p.107 / Chapter 6.1 --- Summary --- p.107 / Chapter 6.2 --- Future Work --- p.109 / Bibliography --- p.117

Hoi, Chu-Hong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 89-103). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Content-based Image Retrieval --- p.1 / Chapter 1.2 --- Relevance Feedback --- p.3 / Chapter 1.3 --- Contributions --- p.4 / Chapter 1.4 --- Organization of This Work --- p.6 / Chapter 2 --- Background --- p.8 / Chapter 2.1 --- Relevance Feedback --- p.8 / Chapter 2.1.1 --- Heuristic Weighting Methods --- p.9 / Chapter 2.1.2 --- Optimization Formulations --- p.10 / Chapter 2.1.3 --- Various Machine Learning Techniques --- p.11 / Chapter 2.2 --- Support Vector Machines --- p.12 / Chapter 2.2.1 --- Setting of the Learning Problem --- p.12 / Chapter 2.2.2 --- Optimal Separating Hyperplane --- p.13 / Chapter 2.2.3 --- Soft-Margin Support Vector Machine --- p.15 / Chapter 2.2.4 --- One-Class Support Vector Machine --- p.16 / Chapter 3 --- Relevance Feedback with Biased SVM --- p.18 / Chapter 3.1 --- Introduction --- p.18 / Chapter 3.2 --- Biased Support Vector Machine --- p.19 / Chapter 3.3 --- Relevance Feedback Using Biased SVM --- p.22 / Chapter 3.3.1 --- Advantages of BSVM in Relevance Feedback --- p.22 / Chapter 3.3.2 --- Relevance Feedback Algorithm by BSVM --- p.23 / Chapter 3.4 --- Experiments --- p.24 / Chapter 3.4.1 --- Datasets --- p.24 / Chapter 3.4.2 --- Image Representation --- p.25 / Chapter 3.4.3 --- Experimental Results --- p.26 / Chapter 3.5 --- Discussions --- p.29 / Chapter 3.6 --- Summary --- p.30 / Chapter 4 --- Optimizing Learning with SVM Constraint --- p.31 / Chapter 4.1 --- Introduction --- p.31 / Chapter 4.2 --- Related Work and Motivation --- p.33 / Chapter 4.3 --- Optimizing Learning with SVM Constraint --- p.35 / Chapter 4.3.1 --- Problem Formulation and Notations --- p.35 / Chapter 4.3.2 --- Learning boundaries with SVM --- p.35 / Chapter 4.3.3 --- OPL for the Optimal Distance Function --- p.38 / Chapter 4.3.4 --- Overall Similarity Measure with OPL and SVM --- p.40 / Chapter 4.4 --- Experiments --- p.41 / Chapter 4.4.1 --- Datasets --- p.41 / Chapter 4.4.2 --- Image Representation --- p.42 / Chapter 4.4.3 --- Performance Evaluation --- p.43 / Chapter 4.4.4 --- Complexity and Time Cost Evaluation --- p.45 / Chapter 4.5 --- Discussions --- p.47 / Chapter 4.6 --- Summary --- p.48 / Chapter 5 --- Group-based Relevance Feedback --- p.49 / Chapter 5.1 --- Introduction --- p.49 / Chapter 5.2 --- SVM Ensembles --- p.50 / Chapter 5.3 --- Group-based Relevance Feedback Using SVM Ensembles --- p.51 / Chapter 5.3.1 --- (x+l)-class Assumption --- p.51 / Chapter 5.3.2 --- Proposed Architecture --- p.52 / Chapter 5.3.3 --- Strategy for SVM Combination and Group Ag- gregation --- p.52 / Chapter 5.4 --- Experiments --- p.54 / Chapter 5.4.1 --- Experimental Implementation --- p.54 / Chapter 5.4.2 --- Performance Evaluation --- p.55 / Chapter 5.5 --- Discussions --- p.56 / Chapter 5.6 --- Summary --- p.57 / Chapter 6 --- Log-based Relevance Feedback --- p.58 / Chapter 6.1 --- Introduction --- p.58 / Chapter 6.2 --- Related Work and Motivation --- p.60 / Chapter 6.3 --- Log-based Relevance Feedback Using SLSVM --- p.61 / Chapter 6.3.1 --- Problem Statement --- p.61 / Chapter 6.3.2 --- Soft Label Support Vector Machine --- p.62 / Chapter 6.3.3 --- LRF Algorithm by SLSVM --- p.64 / Chapter 6.4 --- Experimental Results --- p.66 / Chapter 6.4.1 --- Datasets --- p.66 / Chapter 6.4.2 --- Image Representation --- p.66 / Chapter 6.4.3 --- Experimental Setup --- p.67 / Chapter 6.4.4 --- Performance Comparison --- p.68 / Chapter 6.5 --- Discussions --- p.73 / Chapter 6.6 --- Summary --- p.75 / Chapter 7 --- Application: Web Image Learning --- p.76 / Chapter 7.1 --- Introduction --- p.76 / Chapter 7.2 --- A Learning Scheme for Searching Semantic Concepts --- p.77 / Chapter 7.2.1 --- Searching and Clustering Web Images --- p.78 / Chapter 7.2.2 --- Learning Semantic Concepts with Relevance Feed- back --- p.73 / Chapter 7.3 --- Experimental Results --- p.79 / Chapter 7.3.1 --- Dataset and Features --- p.79 / Chapter 7.3.2 --- Performance Evaluation --- p.80 / Chapter 7.4 --- Discussions --- p.82 / Chapter 7.5 --- Summary --- p.82 / Chapter 8 --- Conclusions and Future Work --- p.84 / Chapter 8.1 --- Conclusions --- p.84 / Chapter 8.2 --- Future Work --- p.85 / Chapter A --- List of Publications --- p.87 / Bibliography --- p.103

Hui Pui Yu. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 158-169). / Abstracts in English and Chinese. / Abstract --- p.2 / Acknowledgements --- p.6 / Chapter 1 --- Introduction --- p.23 / Chapter 1.1 --- Spoken Document Retrieval --- p.24 / Chapter 1.2 --- The Chinese Language and Chinese Spoken Documents --- p.28 / Chapter 1.3 --- Motivation --- p.33 / Chapter 1.3.1 --- Assisting the User in Query Formation --- p.34 / Chapter 1.4 --- Goals --- p.34 / Chapter 1.5 --- Thesis Organization --- p.35 / Chapter 2 --- Multimedia Repository --- p.37 / Chapter 2.1 --- The Cantonese Corpus --- p.37 / Chapter 2.1.1 --- The RealMedia´ёØCollection --- p.39 / Chapter 2.1.2 --- The MPEG-1 Collection --- p.40 / Chapter 2.2 --- The Multimedia Markup Language --- p.42 / Chapter 2.3 --- Chapter Summary --- p.44 / Chapter 3 --- Monolingual Retrieval Task --- p.45 / Chapter 3.1 --- Properties of Cantonese Video Archive --- p.45 / Chapter 3.2 --- Automatic Speech Transcription --- p.46 / Chapter 3.2.1 --- Transcription of Cantonese Spoken Documents --- p.47 / Chapter 3.2.2 --- Indexing Units --- p.48 / Chapter 3.3 --- Known-Item Retrieval Task --- p.49 / Chapter 3.3.1 --- Evaluation ´ؤ Average Inverse Rank --- p.50 / Chapter 3.4 --- Retrieval Model --- p.51 / Chapter 3.5 --- Experimental Results --- p.52 / Chapter 3.6 --- Chapter Summary --- p.53 / Chapter 4 --- The Use of Audio and Video Information for Monolingual Spoken Document Retrieval --- p.55 / Chapter 4.1 --- Video-based Segmentation --- p.56 / Chapter 4.1.1 --- Metric Computation --- p.57 / Chapter 4.1.2 --- Shot Boundary Detection --- p.58 / Chapter 4.1.3 --- Shot Transition Detection --- p.67 / Chapter 4.2 --- Audio-based Segmentation --- p.69 / Chapter 4.2.1 --- Gaussian Mixture Models --- p.69 / Chapter 4.2.2 --- Transition Detection --- p.70 / Chapter 4.3 --- Performance Evaluation --- p.72 / Chapter 4.3.1 --- Automatic Story Segmentation --- p.72 / Chapter 4.3.2 --- Video-based Segmentation Algorithm --- p.73 / Chapter 4.3.3 --- Audio-based Segmentation Algorithm --- p.74 / Chapter 4.4 --- Fusion of Video- and Audio-based Segmentation --- p.75 / Chapter 4.5 --- Retrieval Performance --- p.76 / Chapter 4.6 --- Chapter Summary --- p.78 / Chapter 5 --- Document Expansion for Monolingual Spoken Document Retrieval --- p.79 / Chapter 5.1 --- Document Expansion using Selected Field Speech Segments --- p.81 / Chapter 5.1.1 --- Annotations from MmML --- p.81 / Chapter 5.1.2 --- Selection of Cantonese Field Speech --- p.83 / Chapter 5.1.3 --- Re-weighting Different Retrieval Units --- p.84 / Chapter 5.1.4 --- Retrieval Performance with Document Expansion using Selected Field Speech --- p.84 / Chapter 5.2 --- Document Expansion using N-best Recognition Hypotheses --- p.87 / Chapter 5.2.1 --- Re-weighting Different Retrieval Units --- p.90 / Chapter 5.2.2 --- Retrieval Performance with Document Expansion using TV-best Recognition Hypotheses --- p.90 / Chapter 5.3 --- Document Expansion using Selected Field Speech and N-best Recognition Hypotheses --- p.92 / Chapter 5.3.1 --- Re-weighting Different Retrieval Units --- p.92 / Chapter 5.3.2 --- Retrieval Performance with Different Indexed Units --- p.93 / Chapter 5.4 --- Chapter Summary --- p.94 / Chapter 6 --- Query Expansion for Cross-language Spoken Document Retrieval --- p.97 / Chapter 6.1 --- The TDT-2 Corpus --- p.99 / Chapter 6.1.1 --- English Textual Queries --- p.100 / Chapter 6.1.2 --- Mandarin Spoken Documents --- p.101 / Chapter 6.2 --- Query Processing --- p.101 / Chapter 6.2.1 --- Query Weighting --- p.101 / Chapter 6.2.2 --- Bigram Formation --- p.102 / Chapter 6.3 --- Cross-language Retrieval Task --- p.103 / Chapter 6.3.1 --- Indexing Units --- p.104 / Chapter 6.3.2 --- Retrieval Model --- p.104 / Chapter 6.3.3 --- Performance Measure --- p.105 / Chapter 6.4 --- Relevance Feedback --- p.106 / Chapter 6.4.1 --- Pseudo-Relevance Feedback --- p.107 / Chapter 6.5 --- Retrieval Performance --- p.107 / Chapter 6.6 --- Chapter Summary --- p.109 / Chapter 7 --- Conclusions and Future Work --- p.111 / Chapter 7.1 --- Future Work --- p.114 / Chapter A --- XML Schema for Multimedia Markup Language --- p.117 / Chapter B --- Example of Multimedia Markup Language --- p.128 / Chapter C --- Significance Tests --- p.135 / Chapter C.1 --- Selection of Cantonese Field Speech Segments --- p.135 / Chapter C.2 --- Fusion of Video- and Audio-based Segmentation --- p.137 / Chapter C.3 --- Document Expansion with Reporter Speech --- p.137 / Chapter C.4 --- Document Expansion with N-best Recognition Hypotheses --- p.140 / Chapter C.5 --- Document Expansion with Reporter Speech and N-best Recognition Hypotheses --- p.140 / Chapter C.6 --- Query Expansion with Pseudo Relevance Feedback --- p.142 / Chapter D --- Topic Descriptions of TDT-2 Corpus --- p.145 / Chapter E --- Speech Recognition Output from Dragon in CLSDR Task --- p.148 / Chapter F --- Parameters Estimation --- p.152 / Chapter F.1 --- "Estimating the Number of Relevant Documents, Nr" --- p.152 / Chapter F.2 --- "Estimating the Number of Terms Added from Relevant Docu- ments, Nrt , to Original Query" --- p.153 / Chapter F.3 --- "Estimating the Number of Non-relevant Documents, Nn , from the Bottom-scoring Retrieval List" --- p.153 / Chapter F.4 --- "Estimating the Number of Terms, Selected from Non-relevant Documents (Nnt), to be Removed from Original Query" --- p.154 / Chapter G --- Abbreviations --- p.155 / Bibliography --- p.158