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Searches for pulsars during the past fifty years, have been characterised by two problems making their discovery difficult: i) an increasing volume of data to be searched, and ii) an increasing number of `candidate' pulsar detections arising from that data, requiring analysis. Whilst almost all are caused by noise or interference, these are often indistinguishable from real pulsar detections. Deciding which candidates should be studied is therefore difficult. Indeed it has become known as the `candidate selection problem'. This thesis presents an interdisciplinary study of the selection problem, with the aim of developing a new method able to mitigate it. Specifically for future pulsar surveys undertaken with the Square kilometre Array (SKA). Through a combination of critical literature evaluations, theoretical modelling exercises, and empirical investigations, the selection problem is described in-depth here for the first time. It is shown to be characterised by the dominance of Gaussian distributed noise signals, a factor that no existing selection method accounts for. It also reveals the presence of a significant trend in survey data rates, which suggest that candidate selection is transitioning from an off-line processing procedure, to an on-line, and real-time, decision making process. In response, a new real-time machine learning based method, the GH-VFDT, is introduced in this thesis. The results presented here show that a significant improvement in selection performance can be achieved using the GH-VFDT, which utilises a learning procedure optimised for data characterised by skewed class distributions. Whilst the principled development of new numerical features that maximise the separation between pulsars and Gaussian noise, have also greatly improved GH-VFDT pulsar recall. It is therefore concluded that the sub-optimal performance of existing selection systems, is due to a combination of poor feature design, insensitivity to noise, and an inability to deal with skewed class distributions.

Since past more than hundred years, fluvial geomorphologists all across the globe have been trying to understand the basic phenomena and processes that control the behavioral patterns of streams. A large number of stream classification systems has been proposed till date, but none of them have been accepted universally. Lately, a large amount of efforts have been made to develop bankfull relations for estimating channel geometry that can be employed for stream restoration practices. Focusing on these two objectives, in this study a new stream classification system based on elevation above mean sea level has been developed and later using elevation as one of the independent and nondimensionalising parameters, universal and regional regime equations in dimensionless forms have been developed for predicting channel geometry at bankfull conditions. To accomplish the first objective, 873 field measurement values describing the hydraulic geometry and morphology of streams mainly from Canada, UK and USA were compiled and statistically analyzed. Based on similar mode values of three dimensionless channel variables (aspect ratio, sinuosity and channel slope), several fine elevations ranges were merged to produce the final five elevation ranges. These final five zones formed the basis of the new elevation based classification system and were identified with their unique modal values of dimensionless variables. Performing joint probability distributions on each of these zones, trends in the behavior of channel variables while moving from lowland to upland were observed. For the completion of second objective, 405 data points out of initial 873 points were selected and employed for the development of bankfull relations by using bankfull discharge and watershed variables as the input variables. Regression equations developed for width and depth established bankfull discharge as the only required input variable whereas all other watershed variables were proved out to be relatively insignificant. Channel slope equation did not show any dependence on bankfull discharge and was observed to be influenced only by drainage area and valley slope factors. Later when bankfull discharge was replaced by annual average rainfall as the new input variable, watershed parameters (drainage area, forest cover, urban cover etc.) became significant in bankfull width and depth regression equations. This suggested that bankfull discharge in itself encompasses the effects of all the watershed variables and associated processes and thus is sufficient for estimating channel dimensions. Indeed, bankfull discharge based regression equation demonstrated its strong dependence on watershed and rainfall variables. / Master of Science

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This dissertation introduces novel support vector machines (SVM) for the following traditional and non-traditional learning paradigms: Online classification, Multi-Target Regression, Multiple-Instance classification, and Data Stream classification. Three multi-target support vector regression (SVR) models are first presented. The first involves building independent, single-target SVR models for each target. The second builds an ensemble of randomly chained models using the first single-target method as a base model. The third calculates the targets' correlations and forms a maximum correlation chain, which is used to build a single chained SVR model, improving the model's prediction performance, while reducing computational complexity. Under the multi-instance paradigm, a novel SVM multiple-instance formulation and an algorithm with a bag-representative selector, named Multi-Instance Representative SVM (MIRSVM), are presented. The contribution trains the SVM based on bag-level information and is able to identify instances that highly impact classification, i.e. bag-representatives, for both positive and negative bags, while finding the optimal class separation hyperplane. Unlike other multi-instance SVM methods, this approach eliminates possible class imbalance issues by allowing both positive and negative bags to have at most one representative, which constitute as the most contributing instances to the model. Due to the shortcomings of current popular SVM solvers, especially in the context of large-scale learning, the third contribution presents a novel stochastic, i.e. online, learning algorithm for solving the L1-SVM problem in the primal domain, dubbed OnLine Learning Algorithm using Worst-Violators (OLLAWV). This algorithm, unlike other stochastic methods, provides a novel stopping criteria and eliminates the need for using a regularization term. It instead uses early stopping. Because of these characteristics, OLLAWV was proven to efficiently produce sparse models, while maintaining a competitive accuracy. OLLAWV's online nature and success for traditional classification inspired its implementation, as well as its predecessor named OnLine Learning Algorithm - List 2 (OLLA-L2), under the batch data stream classification setting. Unlike other existing methods, these two algorithms were chosen because their properties are a natural remedy for the time and memory constraints that arise from the data stream problem. OLLA-L2's low spacial complexity deals with memory constraints imposed by the data stream setting, and OLLAWV's fast run time, early self-stopping capability, as well as the ability to produce sparse models, agrees with both memory and time constraints. The preliminary results for OLLAWV showed a superior performance to its predecessor and was chosen to be used in the final set of experiments against current popular data stream methods. Rigorous experimental studies and statistical analyses over various metrics and datasets were conducted in order to comprehensively compare the proposed solutions against modern, widely-used methods from all paradigms. The experimental studies and analyses confirm that the proposals achieve better performances and more scalable solutions than the methods compared, making them competitive in their respected fields.

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