Self concept, acculturation, and fashion orientation

The research begins with a discussion of the worldwide and the Canadian market. The research profiles the examination of the relationship between a person's self concept (as defined by Malhotra) and fashion orientation (as defined by Gutman and Mills), and to understand how these factors are influenced by acculturation, focusing in-depth on their managerial implications. To study these relationships; a random sample of 196 ChineseCanadian female university students living in Canada was given a survey based on Malhotra's self-concept scale, and the SLASIA acculturation scale. Based on multiple regression analysis, findings suggest that the adoption of language and social interaction dimensions of acculturation constructs have significant effects on the relationship between self concept and fashion orientation. This research contributes significantly to both marketing theory and practice. Theoretically, this research develops new insights on the dimensionality of fashion orientation, identifies various moderating effects of acculturation on the relationship of self concept and fashion orientation dimensions, and provides a framework to examine these effects, where results can be generalized across different culture. Practically, marketers can use available findings to improve their understanding of the fashion needs of Chinese-Canadian consumers, and target them based on these findings. The findings provide valuable implications for companies to formulate their fashion marketing strategies for enhance fashion orientation in terms of different dimensions, based on different levels of acculturation.

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.

Crystal Growth and Characterization of Li0.9Mo6O17

Purple bronze Li0.9Mo6O17 has attracted researchers for its low dimensionality and corresponding properties. Although it has been studied for nearly two decades, there are still some unsolved puzzles with this unique material. Single crystals of Li0.9Mo6O17 were grown using the temperature gradient flux technique in this research. The crystal growth was optimized by experimenting different conditions and good quality crystals were obtained. X-ray diffraction results have confirmed the right phase of the crystals. Resistivity measurements and magnetic susceptibility measurements were carried out, and anomalous electronic behaviors were found. All of the samples showed the metal-insulator transition near 20K, followed by behavior that differs from sample to sample: either superconducting, metallic or insulating behavior was observed below 2K. Li0.9Mo6O17 was considered as a quasi-one-dimensional crystal and also a superconducting crystal, which implies a dimensional crossover may occur at the metal-insulator transition. A two-band scenario of the Luttinger liquid model was used to fit the resistivity data and excellent results were achieved, suggesting that the Luttinger theory is a very good candidate for the explanation of the anomalous behavior of Li0.9Mo6O17. In addition, the susceptibility measurements showed Curie paramagnetism and some temperature independent paramagnetism at low temperature. The absence of any anomalous magnetic feature near 20K where the resistivity upturn takes place, suggests that a charge density wave mechanism, which has been proposed by some researchers, is not responsible for the unique properties of Li0.9Mo6O17.

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.