title: A report on child cycling safety

authors: Snape, Holly

subjects: child safety

abstract: The research project identifies and examines various issues related to the cycling  safety of urban school children in Hamilton aged between eight and 15 years of age in  Hamilton. The report outlines, discusses and evaluates the various negative and positive variables that affect the level of cycling safety experienced by urban school children. Research-based recommendations are provided with three purposes in mind. First, some are intended immediately to enhance the effectiveness of existing physical and educational provisions. Second, some are intended to promote discussion of possible amendments to the strategies and overall structure of responsibility and authority of bodies of control, such as local and regional authorities, as well as interest and advocacy groups with a view to achieving enhanced safety provisions through new ideas and approaches . Third, and most fundamentally, these two kinds of recommendations are designed to achieve short-term and sustained long-term reductions in the rates of cycling accidents involving urban school children. As such, the underpinning objective of this report is to create an accessible resource of information and recommendations relevant to any party interested in the safety of child cyclists in urban areas.

text: A report on child cycling safety The research project identifies and examines various issues related to the cycling  safety of urban school children in Hamilton aged between eight and 15 years of age in  Hamilton. The report outlines, discusses and evaluates the various negative and positive variables that affect the level of cycling safety experienced by urban school children. Research-based recommendations are provided with three purposes in mind. First, some are intended immediately to enhance the effectiveness of existing physical and educational provisions. Second, some are intended to promote discussion of possible amendments to the strategies and overall structure of responsibility and authority of bodies of control, such as local and regional authorities, as well as interest and advocacy groups with a view to achieving enhanced safety provisions through new ideas and approaches . Third, and most fundamentally, these two kinds of recommendations are designed to achieve short-term and sustained long-term reductions in the rates of cycling accidents involving urban school children. As such, the underpinning objective of this report is to create an accessible resource of information and recommendations relevant to any party interested in the safety of child cyclists in urban areas.

year: 2001

title: Bagging ensemble selection for regression

authors: Sun, Quan

subjects: Machine learning

abstract: Bagging ensemble selection (BES) is a relatively new ensemble learning strategy. The strategy can be seen as an ensemble of the ensemble selection from libraries of models (ES) strategy. Previous experimental results on binary classiﬁcation problems have shown that using random trees as base classiﬁers, BES-OOB (the most successful variant of BES) is competitive with (and in many cases, superior to) other ensemble learning strategies, for instance, the original ES algorithm, stacking with linear regression, random forests or boosting. Motivated by the promising results in classiﬁcation, this paper examines the predictive performance of the BES-OOB strategy for regression problems. Our results show that the BES-OOB strategy outperforms Stochastic Gradient Boosting and Bagging when using regression trees as the base learners. Our results also suggest that the advantage of using a diverse model library becomes clear when the model library size is relatively large. We also present encouraging results indicating that the non negative least squares algorithm is a viable approach for pruning an ensemble of ensembles.

text: Bagging ensemble selection for regression Bagging ensemble selection (BES) is a relatively new ensemble learning strategy. The strategy can be seen as an ensemble of the ensemble selection from libraries of models (ES) strategy. Previous experimental results on binary classiﬁcation problems have shown that using random trees as base classiﬁers, BES-OOB (the most successful variant of BES) is competitive with (and in many cases, superior to) other ensemble learning strategies, for instance, the original ES algorithm, stacking with linear regression, random forests or boosting. Motivated by the promising results in classiﬁcation, this paper examines the predictive performance of the BES-OOB strategy for regression problems. Our results show that the BES-OOB strategy outperforms Stochastic Gradient Boosting and Bagging when using regression trees as the base learners. Our results also suggest that the advantage of using a diverse model library becomes clear when the model library size is relatively large. We also present encouraging results indicating that the non negative least squares algorithm is a viable approach for pruning an ensemble of ensembles.

year: 2012

title: He pao

authors: Roa, Tom

subjects: Pao

abstract: Ko Te Whare Wānanga O Waikato" te ingoa o tētehi pao nāku i tito hei waiata mā Te Whare Wānanga O Waikato, i te tau 2004.

text: He pao Ko Te Whare Wānanga O Waikato" te ingoa o tētehi pao nāku i tito hei waiata mā Te Whare Wānanga O Waikato, i te tau 2004.

year: 2012-05