by Cheang Sin Man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1990. / Bibliography: leaves 137-140. / ABSTRACT --- p.i / ACKNOWLEDGEMENTS --- p.ii / TABLE OF CONTENTS --- p.iii / Chapter CHAPTER 1 --- INTRODUCTION --- p.1 / Chapter 1.1 --- Lisp as an AI Programming Language --- p.1 / Chapter 1.2 --- Assisting List Processing with Hardware --- p.2 / Chapter 1.3 --- Simulation Study --- p.2 / Chapter 1.4 --- Implementation --- p.3 / Chapter 1.4.1 --- Hardware --- p.3 / Chapter 1.4.2 --- Software --- p.3 / Chapter 1.5 --- Performance --- p.4 / Chapter CHAPTER 2 --- LISP AND EXISTING LISP MACHINES --- p.5 / Chapter 2.1 --- Lisp and its Internal Structure --- p.5 / Chapter 2.1.1 --- The List Structure in Lisp --- p.5 / Chapter 2.1.2 --- Data Types in Lisp --- p.7 / Chapter 2.1.3 --- Lisp Functions --- p.8 / Chapter 2.1.4 --- Storage Management of Lisp --- p.9 / Chapter 2.2 --- Existing Lisp Machines --- p.11 / Chapter 2.2.1 --- Types of AI Architecture --- p.11 / Language-Based architecture --- p.11 / Knowledge-Based architecture --- p.12 / Semantic networks --- p.12 / Chapter 2.2.2 --- Lisp Machines --- p.12 / Solving problems of Lisp --- p.13 / Chapter 2.2.3 --- Classes of Lisp Machines --- p.14 / Two M Lisp machine examples --- p.15 / A class P machine example --- p.17 / A class S machine example --- p.17 / The best class for Lisp --- p.19 / Chapter 2.3 --- Execution Time Analysis of a Lisp System --- p.20 / Chapter 2.3.1 --- CPU Time Statistics --- p.20 / Chapter 2.3.2 --- Statistics Analysis --- p.24 / Chapter CHAPTER 3 --- OVERALL ARCHITECTURE OF THE ASLP --- p.27 / Chapter 3.1 --- An Arithmetical & Symbolical List Processor --- p.27 / Chapter 3.2 --- Multiple Memory Modules --- p.30 / Chapter 3.3 --- Large Number of Registers --- p.31 / Chapter 3.4 --- Multiple Buses --- p.34 / Chapter 3.5 --- Special Function Units --- p.35 / Chapter CHAPTER 4 --- PARALLELISM IN THE ASLP --- p.36 / Chapter 4.1 --- Parallel Data Movement --- p.36 / Chapter 4.2 --- Wide Memory Modules --- p.37 / Chapter 4.3 --- Parallel Memory Access --- p.39 / Chapter 4.3.1 --- Parallelism and Pipelining --- p.39 / Chapter 4.4 --- Pipelined Micro-Instructions --- p.40 / Chapter 4.4.1 --- Memory access pipelining --- p.41 / Chapter 4.5 --- Performance Estimation --- p.44 / Chapter 4.6 --- Parallel Execution with the Host Computer --- p.45 / Chapter CHAPTER 5 --- SIMULATION STUDY OF THE ASLP --- p.47 / Chapter 5.1 --- Why Simulation is needed for the ASLP? --- p.47 / Chapter 5.2 --- The Structure of the HOCB Simulator --- p.48 / Chapter 5.2.1 --- Activity-Oriented Simulation for the ASLP --- p.50 / Chapter 5.3 --- The Hardware Object Declaration Method --- p.50 / Chapter 5.4 --- A Register-Level Simulation of the ASLP --- p.53 / Chapter 5.4.1 --- A List Function Simulation --- p.54 / Chapter CHAPTER 6 --- DESIGN AND IMPLEMENTATION OF THE ASLP --- p.57 / Chapter 6.1 --- Hardware --- p.57 / Chapter 6.1.1 --- Microprogrammable Controller --- p.57 / The instruction cycle of the micro-controller --- p.59 / Chapter 6.1.2 --- Chip Selection and Allocation --- p.59 / Chapter 6.2 --- Software --- p.61 / Chapter 6.2.1 --- Instruction Passing --- p.61 / Chapter 6.2.2 --- Microprogram Development --- p.62 / Microprogram field definition --- p.64 / Micro-assembly language --- p.65 / Macro-instructions --- p.65 / Down-loading of Micro-Codes --- p.66 / Interfacing to C language --- p.66 / A Turbo C Function Library --- p.67 / Chapter CHAPTER 7 --- PERFORMANCE EVALUATION OF THE ASLP …… --- p.68 / Chapter 7.1 --- Micro-Functions in the ASLP --- p.68 / Chapter 7.2 --- Functions in the C Library --- p.71 / Chapter CHAPTER 8 --- FUNCTIONAL EVALUATION OF THE ASLP --- p.77 / Chapter 8.1 --- A Relational Database on the ASLP --- p.77 / Chapter 8.1.1 --- Data Representation --- p.77 / Chapter 8.1.2 --- Performance of the Database System --- p.79 / Chapter 8.2 --- Other Potential Applications --- p.80 / Chapter CHAPTER 9 --- FUTURE DEVELOPMENT OF THE ASLP --- p.81 / Chapter 9.1 --- An Expert System Shell on the ASLP --- p.81 / Chapter 9.1.1 --- Definition of Objects --- p.81 / Chapter 9.1.2 --- Knowledge Representation --- p.84 / Chapter 9.1.3 --- Knowledge Representation in the ASLP --- p.85 / Chapter 9.1.4 --- Overall Structure --- p.88 / Chapter 9.2 --- Reducing the Physical Size by Employing VLSIs --- p.89 / Chapter CHAPTER 10 --- CONCLUSION --- p.92 / Chapter APPENDIX A --- BLOCK DIAGRAM --- p.95 / Chapter APPENDIX B --- ASLP CIRCUIT DIAGRAMS --- p.97 / Chapter APPENDIX C --- ASLP PC-BOARD LAYOUTS --- p.114 / Chapter APPENDIX D --- MICRO-CONTROL SIGNAL ASSIGNMENT --- p.121 / Chapter APPENDIX E --- MICRO-FIELD DEFINITION --- p.124 / Chapter APPENDIX F --- MACRO DEFINITION --- p.133 / Chapter APPENDIX G --- REGISTER ASSIGNMENT --- p.134 / PUBLICATIONS --- p.136 / REFERENCES --- p.137

No description available.

In an exploratory approach to data analysis, it is often useful to consider the observations as generated from a set of latent generators or 'sources' via a generally unknown mapping. Reconstructing sources from their mixtures is an extremely ill-posed problem in general. However, solutions to such inverse problems can, in many cases, be achieved by incorporating prior knowledge about the problem, captured in the form of constraints. This setting is a natural candidate for the application of the Bayesian method- ology, allowing us to incorporate "soft" constraints in a natural manner. This Thesis proposes the use of sparse statistical decomposition methods for ex- ploratory analysis of datasets. We make use of the fact that many natural signals have a sparse representation in appropriate signal dictionaries. The work described in this Thesis is mainly driven by problems in the analysis of large datasets, such as those from functional magnetic resonance imaging of the brain for the neuro-scientific goal of extracting relevant 'maps' from the data. We first propose Bayesian Iterative Thresholding, a general method for solv- ing blind linear inverse problems under sparsity constraints, and we apply it to the problem of blind source separation. The algorithm is derived by maximiz- ing a variational lower-bound on the likelihood. The algorithm generalizes the recently proposed method of Iterative Thresholding. The probabilistic view en- ables us to automatically estimate various hyperparameters, such as those that control the shape of the prior and the threshold, in a principled manner. We then derive an efficient fully Bayesian sparse matrix factorization model for exploratory analysis and modelling of spatio-temporal data such as fMRI. We view sparse representation as a problem in Bayesian inference, following a ma- chine learning approach, and construct a structured generative latent-variable model employing adaptive sparsity-inducing priors. The construction allows for automatic complexity control and regularization as well as denoising. The performance and utility of the proposed algorithms is demonstrated on a variety of experiments using both simulated and real datasets. Experimental results with benchmark datasets show that the proposed algorithms outper- form state-of-the-art tools for model-free decompositions such as independent component analysis.

by Ng Kam Hon. / Thesis (M.Sc.)--Chinese University of Hong Kong, 1992. / Includes bibliographical references (leaves 30-31). / Chapter 1. --- Introduction --- p.1 / Chapter 2 --- Project Management --- p.2 / Chapter 3. --- PERT and CPM --- p.3 / Chapter 4. --- Motivation for the proposed automation --- p.4 / Chapter 5 --- The Project Controller --- p.6 / Chapter 5.1 --- General --- p.6 / Chapter 5.2 --- System Design --- p.7 / Chapter 6. --- Project Preprocessing --- p.10 / Chapter 6.1 --- Determining the Earliest Start and Finish --- p.10 / Chapter 6.2 --- Determining the Latest Start and Finish --- p.11 / Chapter 6.3 --- Calculation of free slack and preliminary Schedule --- p.12 / Chapter 7. --- Job Scheduling --- p.13 / Chapter 7.1 --- The Rule Based System --- p.14 / Chapter 7.2 --- The Searcher --- p.17 / Chapter 8. --- Evaluation of the Project Controller --- p.23 / Chapter 8.1 --- Input Project Details --- p.23 / Chapter 8.2 --- Project analysis reports --- p.23 / Chapter 8.3 --- Job Scheduling capabilities --- p.23 / Chapter 9. --- Discussion --- p.27 / Chapter 9.1 --- Applications --- p.27 / Chapter 9.2 --- Future Enhancement --- p.27 / Chapter 9.3 --- Limitations --- p.28 / Chapter 10. --- Conclusion --- p.29 / Chapter 11. --- References --- p.30

No description available.

With the increasing availability of large amount of data in a wide range of applications, no matter for industry or academia, it becomes crucial to understand the nature of complex raw data, in order to gain more values from data engineering. Although many problems have been successfully solved by some mature machine learning techniques, the problem of learning from imbalanced data continues to be one of the challenges in the field of data engineering and machine learning, which attracted growing attention in recent years due to its complexity. In this thesis, we focus on four aspects of imbalanced data learning and propose solutions to the key problems. The first aspect is about ensemble methods for imbalanced data classification. Ensemble methods, e.g. bagging and boosting, have the advantages to cure imbalanced data by integrated with sampling methods. However, there are still problems in the integration. One problem is that undersampling and oversampling are complementary each other and the sampling ratio is crucial to the classification performance. This thesis introduces a new method HSBagging which is based on bagging with hybrid sampling. Experiments show that HSBagging outperforms other state-of-the-art bagging method on imbalanced data. Another problem is about the integration of boosting and sampling for imbalanced data classification. The classifier weights of existing AdaBoost-based methods are inconsistent with the objective of class imbalance classification. In this thesis, we propose a novel boosting optimization framework GOBoost. This framework can be applied to any boosting-based method for class imbalance classification by simply replacing the calculation of classifier weights. Experiments show that the GOBoost-based methods significantly outperform the corresponding boosting-based methods. The second aspect is about online learning for imbalanced data stream with concept drift. In the online learning scenario, if the data stream is imbalanced, it will be difficult to detect concept drifts and adapt the online learner to them. The ensemble classifier weights are hard to adjust to achieve the balance between the stability and adaptability. Besides, the classier built on samples in size-fixed chunk, which may be highly imbalanced, is unstable in the ensemble. In this thesis, we propose Adaptive Chunk-based Dynamic Weighted Majority (ACDWM) to dynamically weigh the individual classifiers according to their performance on the current data chunk. Meanwhile, the chunk size is adaptively selected by statistical hypothesis tests. Experiments on both synthetic and real datasets with concept drift show that ACDWM outperforms both of the state-of-the-art chunk-based and online methods. In addition to imbalanced data classification, the third aspect is about clustering on imbalanced data. This thesis studies the key problem of imbalanced data clustering called uniform effect within the k-means-type framework, where the clustering results tend to be balanced. Thus, this thesis introduces a new method called Self-adaptive Multi-prototype-based Competitive Learning (SMCL) for imbalanced clusters. It uses multiple subclusters to represent each cluster with an automatic adjustment of the number of subclusters. Then, the subclusters are merged into the final clusters based on a novel separation measure. Experimental results show the efficacy of SMCL for imbalanced clusters and the superiorities against its competitors. Rather than a specific algorithm for imbalanced data learning, the final aspect is about a measure of class imbalanced dataset for classification. Recent studies have shown that imbalance ratio is not the only cause of the performance loss of a classifier in imbalanced data classification. To the best of our knowledge, there is no any measurement about the extent of influence of class imbalance on the classification performance of imbalanced data. Accordingly, this thesis proposes a data measure called Bayes Imbalance Impact Index (B1³) to reflect the extent of influence purely by the factor of imbalance for the whole dataset. As a result we can therefore use B1³ to judge whether it is worth using imbalance recovery methods like sampling or cost-sensitive methods to recover the performance loss of a classifier. The experiments show that B1³ is highly consistent with improvement of F1score made by the imbalance recovery methods on both synthetic and real benchmark datasets. Two ensemble frameworks for imbalanced data classification are proposed for sampling rate selection and boosting weight optimization, respectively. 2. •A chunk-based online learning algorithm is proposed to dynamically adjust the ensemble classifiers and select the chunk size for imbalanced data stream with concept drift. 3. •A multi-prototype competitive learning algorithm is proposed for clustering on imbalanced data. 4. •A measure of imbalanced data is proposed to evaluate how the classification performance of a dataset is influenced by the factor of imbalance.

Call number: LD2668 .T4 1986 H33 / Master of Science / Computing and Information Sciences

There is now more data being created than ever before and this data can be any form of data, textual, multimedia, spatial etc. To process this data, several big data processing platforms have been developed including Hadoop, based on the MapReduce model and LexisNexis’ HPCC systems. In this thesis we evaluate the HPCC Systems framework with a special interest in multimedia data analysis and propose a framework for multimedia data processing. It is important to note that multimedia data encompasses a wide variety of data including but not limited to image data, video data, audio data and even textual data. While developing a unified framework for such wide variety of data, we have to consider computational complexity in dealing with the data. Preliminary results show that HPCC can potentially reduce the computational complexity significantly. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection

In the design of autonomous mobile robots, databases have been used mainly to store information on the environment in which the device is to operate. For most of the models and ready systems, the database when used, is not a stand alone component in the system, rather it is only intended to keep static information on the disposition and properties of objects on the map.

published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy