Given the breadth of constraint satisfaction problems (CSPs) and the wide variety of CSP solvers, it is often very difficult to determine a priori which solving method is best suited to a problem. This work explores the use of machine learning to predict which solving method will be most effective for a given problem. We use four different problem sets to determine the CSP attributes that can be used to determine which solving method should be applied. After choosing an appropriate set of attributes, we determine how well j48 decision trees can predict which solving method to apply. Furthermore, we take a cost sensitive approach such that problem instances where there is a great difference in runtime between algorithms are emphasized. We also attempt to use information gained on one class of problems to inform decisions about a second class of problems. Finally, we show that the additional costs of deciding which method to apply are outweighed by the time savings compared to applying the same solving method to all problem instances.

Stroke rehabilitation is to train the motor function of a patients limb. In this process, functional assessment is of importance, and it is primarily based on a patients task performance. The context of the rehabilitation discussed in this thesis is such that functional assessment is conducted through a computer system and the Internet. In particular, a patient performs the task at home in a haptic virtual environment, and the task performance is transmitted to the therapist over the Internet. One problem with this approach to functional assessment is that a patients mind state is little known to the therapist. This immediately leads to one question, that is, whether an elevated mind state will have some significant effect on the patients task performance? If so, this approach can result in a considerable error. The overall objective of this thesis study was to generate an answer to the aforementioned question. The study focused on a patients elevated fatigue state. The specific objectives of the study include: (i) developing a haptic virtual environment prototype system for functional assessment, (ii) developing a physiological-based inference system for fatigue state, and (iii) performing an experiment to generate knowledge regarding the fatigue effect on task performance. With a limited resource in recruiting patients in the experiment, the study conducted few experiments on patients but mostly on healthy subjects. The study has concluded: (1) the proposed haptic virtual environment system is effective for the wrist coordination task and is likely promising to other tasks, (2) the accuracy of proposed fatigue inference system achieves 89.54%, for two levels of fatigue state, which is promising, (3) the elevated fatigue state significantly affects task performance in the context of wrist coordination task, and (4) the accuracy of the individual-based inference approach is significantly higher than that of the group-based inference approach. The main contributions of the thesis are (1) generation of the new knowledge regarding the fatigue effect on task performance in the context of home-based rehabilitation, (2) provision of the new fatigue inference system with the highest accuracy in comparison with the existing approaches in literature, and (3) generation of the new knowledge regarding the difference between the individual-based inference and group-based inference approaches.

This thesis is focused on investigating the predictability of exchange rate returns on monthly and daily frequency using models that have been mostly developed in the machine learning field. The forecasting performance of these models will be compared to the Random Walk, which is the benchmark model for financial returns, and the popular autoregressive process. The machine learning models that will be used are Regression trees, Random Forests, Support Vector Regression (SVR), Least Absolute Shrinkage and Selection Operator (LASSO) and Bayesian Additive Regression trees (BART). A characterizing feature of financial returns data is the presence of volatility clustering, i.e. the tendency of persistent periods of low or high variance in the time series. This is in disagreement with the machine learning models which implicitly assume a constant variance. We therefore extend these models with the most widely used model for volatility clustering, the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) process. This allows us to jointly estimate the time varying variance and the parameters of the machine learning using an iterative procedure. These GARCH-extended machine learning models are then applied to make one-step-ahead prediction by recursive estimation that the parameters estimated by this model are also updated with the new information. In order to predict returns, information related to the economic variables and the lagged variable will be used. This study is repeated on three different exchange rate returns: EUR/SEK, EUR/USD and USD/SEK in order to obtain robust results. Our result shows that machine learning models are capable of forecasting exchange returns both on daily and monthly frequency. The results were mixed, however. Overall, it was GARCH-extended SVR that shows great potential for improving the predictive performance of the forecasting of exchange rate returns.

Taiwan is surrounded by ocean, thus the ocean transportation has become the necessary support of Taiwan's economy. Due to this fact, this research provides a system based on cloud computing and distributed storage which is applied to compute large amount of data provided by many sensors on the sea in order to diagnose the existence of possible magnetized invaders. We use Hadoop platform from Apache Foundation to proceed distributable K-means clustering computation to process the data collected f rom many sensor nodes containing DGPS and magnetic sensors. With these data, it is possible to diagnose the existence and the moving direction of the possible invader. And the result can be return to remote monitoring terminal. Not only K-means can detect the irregularity of any axis of the magnetic field well, but also this system obtain good reliability and performance by Hadoop platform. The goal system can detect the irregularity of any axis of the magnetic field well enough by deploying K-Means clustering and obtain good reliability and performance by Hadoop platform.

A motion planner finds a sequence of potential motions for a robot to transit from an initial to a goal state. To deal with the intractability of this problem, a class of methods known as sampling-based planners build approximate representations of potential motions through random sampling. This selective random exploration of the space has produced many remarkable results, including solving many previously unsolved problems. Sampling-based planners usually represent the motions as a graph (e.g., the Probabilistic Roadmap Methods or PRMs), or as a tree (e.g., the Rapidly exploring Random Tree or RRT). Although many sampling-based planners have been proposed, we do not know how to select among them because their different sampling biases make their performance depend on the features of the planning space. Moreover, since a single problem can contain regions with vastly different features, there may not exist a simple exploration strategy that will perform well in every region. Unfortunately, we lack quantitative tools to analyze problem features and planners performance that would enable us to match planners to problems. We introduce novel metrics for the analysis of problem features and planner performance at multiple levels: node level, global level, and region level. At the node level, we evaluate how new samples improve coverage and connectivity of the evolving model. At the global level, we evaluate how new samples improve the structure of the model. At the region level, we identify groups or regions that share similar features. This is a set of general metrics that can be applied in both graph-based and tree-based planners. We show several applications for these tools to compare planners, to decide whether to stop planning or to switch strategies, and to adjust sampling in different regions of the problem.

Thesis (M. Sc.)--University of Alberta, 2009. / Title from PDF file main screen (viewed on Nov. 27, 2009). "A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science, Department of Computing Science, University of Alberta." Includes bibliographical references.

Thesis (Ph. D.)--Rutgers University, 2009. / "Graduate Program in Computer Science." Includes bibliographical references (p. 100-104).

Thesis (Ph. D.)--Stanford University, 1985. / Cover title. "September 1985." Includes bibliographical references.

Thesis (M.Phil.)--City University of Hong Kong, 2009. / "Submitted to Department of Computer Science in partial fulfillment of the requirements for the degree of Master of Philosophy." Includes bibliographical references (leaves [152]-163)

Thesis (M.S.)--Brigham Young University. Dept. of Computer Science, 2004. / Includes bibliographical references (p. 117-121).