title: An Empirical Comparison of Exact Nearest Neighbour Algorithms

authors: Frank, Eibe

subjects: computer science

abstract: Bagging is an ensemble learning method that has proved to be a useful tool in the arsenal of machine learning practitioners. Commonly applied in conjunction with decision tree learners to build an ensemble of decision trees, it often leads to reduced errors in the predictions when compared to using a single tree. A single tree is built from a training set of size N. Bagging is based on the idea that, ideally, we would like to eliminate the variance due to a particular training set by combining trees built from all training sets of size N. However, in practice, only one training set is available, and bagging simulates this platonic method by sampling with replacement from the original training data to form new training sets. In this paper we pursue the idea of sampling from a kernel density estimator of the underlying distribution to form new training sets, in addition to sampling from the data itself. This can be viewed as “smearing out” the resampled training data to generate new datasets, and the amount of “smear” is controlled by a parameter. We show that the resulting method, called “input smearing”, can lead to improved results when compared to bagging. We present results for both classification and regression problems.

text: An Empirical Comparison of Exact Nearest Neighbour Algorithms Bagging is an ensemble learning method that has proved to be a useful tool in the arsenal of machine learning practitioners. Commonly applied in conjunction with decision tree learners to build an ensemble of decision trees, it often leads to reduced errors in the predictions when compared to using a single tree. A single tree is built from a training set of size N. Bagging is based on the idea that, ideally, we would like to eliminate the variance due to a particular training set by combining trees built from all training sets of size N. However, in practice, only one training set is available, and bagging simulates this platonic method by sampling with replacement from the original training data to form new training sets. In this paper we pursue the idea of sampling from a kernel density estimator of the underlying distribution to form new training sets, in addition to sampling from the data itself. This can be viewed as “smearing out” the resampled training data to generate new datasets, and the amount of “smear” is controlled by a parameter. We show that the resulting method, called “input smearing”, can lead to improved results when compared to bagging. We present results for both classification and regression problems.

year: 2007

title: Flowering of kiwifruit (Actinidia deliciosa) is reduced by long photoperiods

authors: Snelgar, W. P.

subjects: winter chilling

abstract: Mature kiwifruit (Actinidia deliciosa ‘Hayward’) vines grown under standard orchard management were exposed to 16-h photoperiods from the longest day in summer until after leaf fall in autumn. Photoperiod extension was achieved with tungsten halogen lamps that produced 2–8 µmols m–2 s–1 photosynthetically active radiation. Long day treatments did not affect fruit dry matter or fruit weight at harvest during the growing season that the treatments were applied or during the following growing season. However, flowering was reduced by 22% during the spring following treatment application. As this reduction in flowering was not accompanied by a decrease in budbreak, the long day effect is not consistent with a delay in the onset of winter chilling. It is suggested therefore, that the observed reduction in flowering may be because of a diminution of floral evocation.

text: Flowering of kiwifruit (Actinidia deliciosa) is reduced by long photoperiods Mature kiwifruit (Actinidia deliciosa ‘Hayward’) vines grown under standard orchard management were exposed to 16-h photoperiods from the longest day in summer until after leaf fall in autumn. Photoperiod extension was achieved with tungsten halogen lamps that produced 2–8 µmols m–2 s–1 photosynthetically active radiation. Long day treatments did not affect fruit dry matter or fruit weight at harvest during the growing season that the treatments were applied or during the following growing season. However, flowering was reduced by 22% during the spring following treatment application. As this reduction in flowering was not accompanied by a decrease in budbreak, the long day effect is not consistent with a delay in the onset of winter chilling. It is suggested therefore, that the observed reduction in flowering may be because of a diminution of floral evocation.

year: 2007

title: Te Rau Matatini: Māori mental health workforce
development

authors: Hirini, Paul

subjects: Maori

abstract: Outlines the development and current projects of Te Rau Matatini, a charitable trust aiming to strengthen the Maori mental health workforce.

text: Te Rau Matatini: Māori mental health workforce
development Outlines the development and current projects of Te Rau Matatini, a charitable trust aiming to strengthen the Maori mental health workforce.

year: 2003