title: Innovation and Growth: Theoretical Models and Analytical Simulations of Spatial, Clustering and Competition Effects

authors: Bond-Smith, Steven Craig

subjects: Endogenous Growth

abstract: This thesis expands upon endogenous growth theory to incorporate a more detailed understanding of innovation. The similarities in modelling techniques between endogenous growth theory and the New Economic Geography (NEG) in the existing literature creates an opportunity to incorporate features of innovation within a model of both geography and growth. The thesis explores three key areas. Firstly, innovation is based on knowledge spillovers. But knowledge spillovers are also subject to spatial characteristics in its transfer between firms, locations, regions and nations. Similarly, knowledge is used by innovators in many sectors, not just the sector where the knowledge was originally developed. As a result, innovating firms have an incentive to agglomerate alongside other innovators to benefit from each other’s knowledge. The geographic constraints of knowledge spillovers are incorporated into a regional model of growth with creative destruction. However, spillovers between industries are not equal. Industries or firms producing products which are closely related have a strong incentive to locate in close proximity to benefit from related-technology spillovers. Some of these related innovation clusters could potentially be isolated, but sustainable due to knowledge transfer between related firms within the cluster. It is this innovation clustering which could provide the opportunity for distant or isolated economies to sustain high levels of productivity. Lastly, the thesis considers a way in which firms enter a market such that entry is not necessarily available to all potential entrants. This limit on the market supplied by innovating firms is characterised by discrete instead of free entry. Entry by discrete firms allows entering firms to invest in innovation in response to actual (instead of potential) competitors. They are able to escape competition by sustaining incremental innovations. Greater competition in larger markets has greater investment in innovation by all participants. This thesis provides new insights for endogenous growth theory, emphasising the need for policies regarding innovation and economic growth that are industry and location specific.

text: Innovation and Growth: Theoretical Models and Analytical Simulations of Spatial, Clustering and Competition Effects This thesis expands upon endogenous growth theory to incorporate a more detailed understanding of innovation. The similarities in modelling techniques between endogenous growth theory and the New Economic Geography (NEG) in the existing literature creates an opportunity to incorporate features of innovation within a model of both geography and growth. The thesis explores three key areas. Firstly, innovation is based on knowledge spillovers. But knowledge spillovers are also subject to spatial characteristics in its transfer between firms, locations, regions and nations. Similarly, knowledge is used by innovators in many sectors, not just the sector where the knowledge was originally developed. As a result, innovating firms have an incentive to agglomerate alongside other innovators to benefit from each other’s knowledge. The geographic constraints of knowledge spillovers are incorporated into a regional model of growth with creative destruction. However, spillovers between industries are not equal. Industries or firms producing products which are closely related have a strong incentive to locate in close proximity to benefit from related-technology spillovers. Some of these related innovation clusters could potentially be isolated, but sustainable due to knowledge transfer between related firms within the cluster. It is this innovation clustering which could provide the opportunity for distant or isolated economies to sustain high levels of productivity. Lastly, the thesis considers a way in which firms enter a market such that entry is not necessarily available to all potential entrants. This limit on the market supplied by innovating firms is characterised by discrete instead of free entry. Entry by discrete firms allows entering firms to invest in innovation in response to actual (instead of potential) competitors. They are able to escape competition by sustaining incremental innovations. Greater competition in larger markets has greater investment in innovation by all participants. This thesis provides new insights for endogenous growth theory, emphasising the need for policies regarding innovation and economic growth that are industry and location specific.

year: 2014

title: Feature subset selection: a correlation based filter approach

authors: Hall, Mark A.

subjects: computer science

abstract: Recent work has shown that feature subset selection can have a position affect on the performance of machine learning algorithms. Some algorithms can be slowed or their performance adversely affected by too much data some of which may be irrelevant or redundant to the learning task. Feature subset selection, then, is a method of enhancing the performance of learning algorithms, reducing the hypothesis search space, and, in some cases, reducing the storage requirement. This paper describes a feature subset selector that uses a correlation based heuristic to determine the goodness of feature subsets, and evaluates its effectiveness with three common ML algorithms: a  decision tree inducer (C4.5), a naive Bayes classifier, and an instance based learner(IBI). Experiments using a number of standard data sets drawn from real and artificial domains are presented. Feature subset selection gave significant improvement for all three algorithms; C4.5 generated smaller decision trees.

text: Feature subset selection: a correlation based filter approach Recent work has shown that feature subset selection can have a position affect on the performance of machine learning algorithms. Some algorithms can be slowed or their performance adversely affected by too much data some of which may be irrelevant or redundant to the learning task. Feature subset selection, then, is a method of enhancing the performance of learning algorithms, reducing the hypothesis search space, and, in some cases, reducing the storage requirement. This paper describes a feature subset selector that uses a correlation based heuristic to determine the goodness of feature subsets, and evaluates its effectiveness with three common ML algorithms: a  decision tree inducer (C4.5), a naive Bayes classifier, and an instance based learner(IBI). Experiments using a number of standard data sets drawn from real and artificial domains are presented. Feature subset selection gave significant improvement for all three algorithms; C4.5 generated smaller decision trees.

year: 1997

title: Verbs in Māori: The problem of definitional criteria

authors: Whaanga, Hēmi

subjects: te reo Māori

abstract: A range of different types of criteria has been applied at different points in the history of the classification of verbs in Māori. These have included syntactic and morphological criteria, semantic criteria and derivational criteria. Unfortunately, these criteria have not always been applied consistently and this has led to considerable disagreement about the classification of verbs in Māori. In this paper, different accounts of verb classification in Māori are critically examined and it is argued that some of the disagreements can be resolved by acknowledging (a) that words in Māori may be assigned to different word classes in different contexts, and (b) that word classes and sub-classes should be determined on the basis of morphological and syntactic criteria with semantic and derivational information playing a role only in so far as they have observable syntactic and/or morphological consequences.

text: Verbs in Māori: The problem of definitional criteria A range of different types of criteria has been applied at different points in the history of the classification of verbs in Māori. These have included syntactic and morphological criteria, semantic criteria and derivational criteria. Unfortunately, these criteria have not always been applied consistently and this has led to considerable disagreement about the classification of verbs in Māori. In this paper, different accounts of verb classification in Māori are critically examined and it is argued that some of the disagreements can be resolved by acknowledging (a) that words in Māori may be assigned to different word classes in different contexts, and (b) that word classes and sub-classes should be determined on the basis of morphological and syntactic criteria with semantic and derivational information playing a role only in so far as they have observable syntactic and/or morphological consequences.

year: 2000-10