Opening truth to imagination : the pragmatism of John Dewey and Richard Rorty

This study explores in a comparative way the works of two American pragmatist philosophers-John Dewey and Richard Rorty. I have provided a reading of their broader works in order to offer what I hope is a successful sympathetic comparison where very few exist. Dewey is often viewed as the central hero in the classical American pragmatic tradition, while Rorty, a contemporary pragmatist, is viewed as some sort of postmodern villain. I show that the different approaches by the two philosophers-Dewey's experiential focus versus Rorty's linguistic focus-exist along a common pragmatic continuum, and that much of the critical scholarship that pits the two pragmatists against each other has actually created an unwarranted dualism between experience and language. I accomplish this task by following the critical movement by each of the pragmatists through their respective reworking of traditional absolutist truth conceptions toward a more aesthetical, imaginative position. I also show how this shift or "turning" represents an important aspect of the American philosophical tradition-its aesthetic axis. I finally indicate a role for liberal education (focusing on higher nonvocational education) in accommodating this turning, a turning that in the end is necessitated by democracy's future trajectory

Educating for love of wisdom : John Dewey and Simone Weil

The writings of John Dewey (1859-1952) and Simone Weil (1909-1943) were analyzed with a view to answering 3 main questions: What is wisdom? How is wisdom connected to experience? How does one educate for a love of wisdom? Using a dialectical method whereby Dewey (a pragmatist) was critiqued by Weil (a Christian Platonist) and vice versa, commonalities and differences were identified and clarified. For both, wisdom involved the application of thought to specific, concrete problems in order to secure a better way of life. For Weil, wisdom was centered on a love of truth that involved a certain way of applying one's attention to a concrete or theoretical problem. Weil believed that nature was subject to a divine wisdom and that a truly democratic society had supernatural roots. Dewey believed that any attempt to move beyond nature would stunt the growth of wisdom. For him, wisdom could be nourished only by natural streams-even if some ofthem were given a divine designation. For both, wisdom emerged through the discipline of work understood as intelligent activity, a coherent relationship between thinking and acting. Although Weil and Dewey differed on how they distinguished these 2 activities, they both advocated a type of education which involved practical experience and confronted concrete problems. Whereas Dewey viewed each problem optimistically with the hope of solving it, Weil saw wisdom in, contemplating insoluble contradictions. For both, educating for a love of wisdom meant cultivating a student's desire to keep thinking in line with acting-wanting to test ideas in action and striving to make sense of actions observed.

From absolutes to aesthetics : John Dewey and the art of experience /

In this thesis I sought to capture something of the integrity of John Dewey's larger vision. While recognizing this to be a difficult challenge, I needed to clear some of the debris of an overly narrow reading of Dewey's works by students of education. The tendency of reducing Dewey's larger philosophical vision down to neat theoretical snap shots in order to prop up their particular social scientific research, was in my estimation slowly damaging the larger integrity of Dewey's vast body of work. It was, in short, killing off the desire to read big works, because doing so was not necessary to satisfying the specialized interests of social scientific research. In this thesis then I made a plea for returning the Humanities to the center of higher education. It is there that students learn how to read and to think—skills required to take on someone of Dewey's stature. I set out in this thesis to do just that. I took Dewey's notion of experience as the main thread connecting all of his philosophy, and focused on two large areas of inquiry, science and its relation to philosophy, and aesthetic experience. By exploring in depth Dewey's understanding of human experience as it pertains to day-to-day living, my call was for a heightened mode of artful conduct within our living contexts. By calling on the necessity of appreciating the more qualitative dimensions of lived experience, I was hoping that students engaged in the Social Sciences might begin to bolster their research interests with more breadth and depth of reading and critical insight. I expressed this as being important to the survival and intelligent flourishing of democratic conduct.

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.

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.

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.