title: Racing committees for large datasets

authors: Frank, Eibe

subjects: Machine learning

abstract: This paper proposes a method for generating classifiers from large datasets by building a committee of simple base classifiers using a standard boosting algorithm.  It allows the processing of large datasets even if the underlying base learning algorithm cannot efficiently do so.  The basic idea is to split incoming data into chunks and build a committee based on classifiers build from these individual chunks [3].  Our method extends earlier work in two ways:  (a) the best chunk size is chosen automatically by racing committees corresponding to different chunk sizes, and (b) the committees are pruned adaptively to keep the size of each individual committee as small as possible without negatively affecting accuracy.  This paper shows that choosing an appropriate chunk size automatically is important because the accuracy of the resulting committee can vary significantly with the chunk size.  It also shows that pruning is crucial to make the method practical for large datasets in terms of running time and memory requirements.  Surprisingly, the results demonstrate that pruning can also improve accuracy.

text: Racing committees for large datasets This paper proposes a method for generating classifiers from large datasets by building a committee of simple base classifiers using a standard boosting algorithm.  It allows the processing of large datasets even if the underlying base learning algorithm cannot efficiently do so.  The basic idea is to split incoming data into chunks and build a committee based on classifiers build from these individual chunks [3].  Our method extends earlier work in two ways:  (a) the best chunk size is chosen automatically by racing committees corresponding to different chunk sizes, and (b) the committees are pruned adaptively to keep the size of each individual committee as small as possible without negatively affecting accuracy.  This paper shows that choosing an appropriate chunk size automatically is important because the accuracy of the resulting committee can vary significantly with the chunk size.  It also shows that pruning is crucial to make the method practical for large datasets in terms of running time and memory requirements.  Surprisingly, the results demonstrate that pruning can also improve accuracy.

year: 2002-06-01

title: Effect of irrigation on yield and fruit quality of rabbiteye blueberry growing on mineral soils in New Zealand

authors: Kaneko, Teruko

abstract: An experiment was conducted to determine the effect of irrigation on fruit yield and quality, and to provide recommendations for the appropriate level of irrigation for 11-year-old Vaccinium ashei Reade var. ‘Maru’ (a Rabbiteye blueberry cultivar), growing on Horotiu silt loam at Oakberry Orchard near Hamilton, New Zealand. Water was applied twice daily by drip irrigation. The treatments were no irrigation, normal irrigation (one-drip line per row), and additional irrigation (two-drip lines per row). During the observation period (November 2013 to April 2014), rainfall was below normal, particularly during fruit ripening and harvest in February and March when drought conditions created differences in soil moisture vertically and horizontally among the three treatments.

text: Effect of irrigation on yield and fruit quality of rabbiteye blueberry growing on mineral soils in New Zealand An experiment was conducted to determine the effect of irrigation on fruit yield and quality, and to provide recommendations for the appropriate level of irrigation for 11-year-old Vaccinium ashei Reade var. ‘Maru’ (a Rabbiteye blueberry cultivar), growing on Horotiu silt loam at Oakberry Orchard near Hamilton, New Zealand. Water was applied twice daily by drip irrigation. The treatments were no irrigation, normal irrigation (one-drip line per row), and additional irrigation (two-drip lines per row). During the observation period (November 2013 to April 2014), rainfall was below normal, particularly during fruit ripening and harvest in February and March when drought conditions created differences in soil moisture vertically and horizontally among the three treatments.

year: 2014

title: Māori and Psychology Research Unit (MPRU) annual report 2018-2021

authors: Masters-Awatere, Bridgette

subjects: Māori and Psychology Research Unit

abstract: Annual report of the Māori and Psychology Research Unit (MPRU) 2018-2021. The unit was established in August of 1997. The unit is designed to provide a catalyst and support network for enhancing research concerning the psychological needs, aspirations, and priorities of Māori. The MPRU is well situated to draw together skilled and experienced interdisciplinary research groups by networking and establishing working relationships with staff and students within the Division of Arts, Law, Psychology and Social Science, the University, and the wider community.

text: Māori and Psychology Research Unit (MPRU) annual report 2018-2021 Annual report of the Māori and Psychology Research Unit (MPRU) 2018-2021. The unit was established in August of 1997. The unit is designed to provide a catalyst and support network for enhancing research concerning the psychological needs, aspirations, and priorities of Māori. The MPRU is well situated to draw together skilled and experienced interdisciplinary research groups by networking and establishing working relationships with staff and students within the Division of Arts, Law, Psychology and Social Science, the University, and the wider community.

year: 2022-09-02