In this dissertation, the performance of the newly developed Fuzzy Regression analysis is explored in various ways. First, the Fuzzy Regression model is compared with the popular nonlinear Self-Exciting Threshold Autoregressive (SETAR) model for forecasting high frequency financial data. Second, we develop Bayesian Fuzzy Regression by using Bayesian Posterior Odds analysis to determine the number of clusters for the fuzzy regression, and fitting Bayesian regressions over each cluster. A careful Monte Carlo experiment indicates that the use of Bayesian Posterior Odds in the context of Fuzzy Regression performs extremely well. Both small sample applications and a large cross sectional case study of the South African equivalence scales then provide strong support to this Bayesian Fuzzy Regression analysis. The advantages of using the Bayesian Fuzzy Regression include its ability to capture nonlinearities in the data in a flexible semi-parametric way, while avoiding the "curse of dimensionality" associated with nonparametric kernel regression.

The model training algorithm is a critical component in the statistical pattern recognition approaches which are based on the Bayes decision theory. Conventional applications of the Bayes decision theory usually assume uniform error cost and result in a ubiquitous use of the maximum a posteriori (MAP) decision policy and the paradigm of distribution estimation as practice in the design of a statistical pattern recognition system. The minimum classification error (MCE) training method is proposed to overcome some substantial limitations for the conventional distribution estimation methods. In this thesis, three aspects of the MCE method are generalized. First, an optimal classifier/recognizer design framework is constructed, aiming at minimizing non-uniform error cost.A generalized training criterion named weighted MCE is proposed for pattern and speech recognition tasks with non-uniform error cost. Second, the MCE method for speech recognition tasks requires appropriate management of multiple recognition hypotheses for each data segment. A modified version of the MCE method with a new approach to selecting and organizing recognition hypotheses is proposed for continuous phoneme recognition. Third, the minimum verification error (MVE) method for detection-based automatic speech recognition (ASR) is studied. The MVE method can be viewed as a special version of the MCE method which aims at minimizing detection/verification errors. We present many experiments on pattern recognition and speech recognition tasks to justify the effectiveness of our generalizations.

Road incidents and incident induced traffic congestions are a big threat to the mobility and safety of our daily life. Timely and accurate incident detection using automated incident detection (AID) systems is essential to effectively tackle incident induced congestion problems and to improve traffic management. The core of an AID system is an incident detection algorithm that interprets real time traffic data and makes decision on incidents. / Literature review of existing AID algorithms and their applications reveals that 1) there is no single freeway algorithm that can fulfil the universality aspect of incident detection which is required by the advanced traffic management systems, and 2) how to achieve the effective and stable arterial road incident detection remains a big issue of AID research. In addition, there exists a strong need for incorporating existing expert traffic knowledge into AID algorithms to enhance incident detection performance. / Thesis (PhDTransportSystemsEngineering)--University of South Australia, 2005.

Thesis (M.S.)--University of Texas at El Paso, 2007. / Title from title screen. Vita. CD-ROM. Includes bibliographical references. Also available online.

Project (M.S.)--Brigham Young University. Dept. of Statistics, 2007. / Includes bibliographical references (p. 60-61).

Thesis (Ph. D. ) -- McMaster University, 1998. / Includes bibliographical references. Also available via World Wide Web.

Thesis (Ph.D.)--George Mason University, 1999. / Includes bibliographical references (leaves [180]-189).

Thesis (Ph. D.)--University of New South Wales, 2003. / Also available online.

Thesis (M. Eng. (Electrical, Electronic and Computer Engineering))--University of Pretoria, 2007. / Includes bibliographical references (leaves 107-112).

Thesis (Ph. D.)--University of Sydney, 2009. / Title from title screen (viewed May 1, 2009) Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy Economics to the Faculty of Economics and Business, University of Sydney. Degree awarded 2009; thesis submitted 2008. Bibliography: leaves 93-97. Also available in print form.