Comparison of classification ability of hyperball algorithms to neural network and k-nearest neighbour algorithms

The main focus of this thesis is to evaluate and compare Hyperbalilearning algorithm (HBL) to other learning algorithms. In this work HBL is compared to feed forward artificial neural networks using back propagation learning, K-nearest neighbor and 103 algorithms. In order to evaluate the similarity of these algorithms, we carried out three experiments using nine benchmark data sets from UCI machine learning repository. The first experiment compares HBL to other algorithms when sample size of dataset is changing. The second experiment compares HBL to other algorithms when dimensionality of data changes. The last experiment compares HBL to other algorithms according to the level of agreement to data target values. Our observations in general showed, considering classification accuracy as a measure, HBL is performing as good as most ANn variants. Additionally, we also deduced that HBL.:s classification accuracy outperforms 103's and K-nearest neighbour's for the selected data sets.

Emergence of conscious awareness of underlying verbal and visual rules over time in moderate closed-head trauma patients

Please consult the paper edition of this thesis to read. It is available on the 5th Floor of the Library at Call Number: Z 9999.5 E38 L64 2008

Management Material: Understanding the Contradictory Perspectives of Lower Level Managers in a Canadian Call Centre

Call centres have emerged during a time of rapid technological change and represent a form of ready employment for those seeking to replace or supplement "traditional" forms of employment. Call centre work is considered characteristic of the kinds of service work available in the new economy. This paper examines the experiences and practices of lower level managers in a call centre in southern Ontario. Findings are based on analysis of semi-structured interviews. The findings suggest that lower level managers resolve the contradictory social space they occupy by aligning themselves primarily with more powerful executives, in part because they know this might lead to increased job security. The implications of this trend for building a strong labour movement capable of combating neoliberal discourses regarding the need for work restructuring are discussed.

Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TM

Genetic Programming (GP) is a widely used methodology for solving various computational problems. GP's problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages for object classification is presented. Two main GP approaches for visual pattern classification, namely the block-classifiers and the pixel-classifiers, were studied. Results showed that the pixel-classifiers generally performed better. Using these results, a suitable language was selected for the real-time implementation. Synthetic video data was used in the experiments. The goal of the experiments was to evolve a unique classifier for each texture pattern that existed in the video. The experiments revealed that the system was capable of correctly tracking the textures in the video. The performance of the system was on-par with real-time requirements.

The Hidden Truths of Self-Presentation, Self-Disclosure, and Deception on Online Dating Profiles: A Call for Change

The way in which individuals seek romantic partners has changed considerably in the past decades, most notably through online dating sites (ODSs). Despite the possibility of misrepresentation amongst client users, such sites continue to grow in popularity because ODSs provide a large pool from which individuals can select and attract potential partners. While much research has been undertaken on ODSs, little empirical research has examined postsecondary students’ use of ODSs. Therefore, this study sought to investigate why postsecondary students have become involved with and how they present themselves on ODSs. The researcher surveyed 20 postsecondary students and conducted in-depth interviews with 2 participants who use ODSs. Although the limited sample prevented results from being generalized, quantitative and qualitative analyses suggest that participants became involved with ODSs for various purposes, such as seeking long-term relationships and/or marriage partners, or simply exploring or visiting ODSs out of curiosity. Findings indicate that ODS users’ physical appearance and/or “attractiveness” is considered the strongest predictor of relationship success. The study discusses how participants’ self-presentation affects outcomes of ODS usage, particularly when negative self-identification and presentation corresponding to factors such as individuals’ weight and age are taken into account.

The Clinical Translation Gap in Child Health Exercise Research: A Call for Disruptive Innovation

In children, levels of play, physical activity, and fitness are key indicators of health and disease and closely tied to optimal growth and development. Cardiopulmonary exercise testing (CPET) provides clinicians with biomarkers of disease and effectiveness of therapy, and researchers with novel insights into fundamental biological mechanisms reflecting an integrated physiological response that is hidden when the child is at rest. Yet the growth of clinical trials utilizing CPET in pediatrics remains stunted despite the current emphasis on preventative medicine and the growing recognition that therapies used in children should be clinically tested in children. There exists a translational gap between basic discovery and clinical application in this essential component of child health. To address this gap, the NIH provided funding through the Clinical and Translational Science Award (CTSA) program to convene a panel of experts. This report summarizes our major findings and outlines next steps necessary to enhance child health exercise medicine translational research. We present specific plans to bolster data interoperability, improve child health CPET reference values, stimulate formal training in exercise medicine for child health care professionals, and outline innovative approaches through which exercise medicine can become more accessible and advance therapeutics across the child health spectrum.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel metaheuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS metaheuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and determinis- tic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel meta–heuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS meta–heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Roll Call for Fires and Alarms for Citizens' Hose Company No. 1

Roll call for fires and alarms for Citizens’ Hose Company No. 1. (1 double sided printed page). H.K. Woodruff is listed as a member, 1883.