title: Maori & Psychology Research Unit annual report 2007

authors: Rua, Mohi

subjects: Maori and Psychology Research Unit

abstract: Annual report of the Maori and Psychology Research Unit (MPRU) 2007. 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 Maori people. 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 Faculty of Arts and Social Sciences, the University, and the wider community.

text: Maori & Psychology Research Unit annual report 2007 Annual report of the Maori and Psychology Research Unit (MPRU) 2007. 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 Maori people. 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 Faculty of Arts and Social Sciences, the University, and the wider community.

year: 2008

title: Mokopuna rising: Developing a best practice for early intervention in whanau violence

authors: Cooper, Erana

subjects: Maori

abstract: In a very difficult meeting, an extremely unpleasant man was concerned about the possible loss of his power and the continuation of his plans and dreams for the future. And I said, yeah well, you should be worried, because according to your philosophy, when you’re dead, you’re dead. But when I’m dead, my mokopuna will be rising. (Personal Communication, Rob Cooper, 2007) 
“Mokopuna Rising” is about claiming space for Maori to define best practice for reducing and avoiding whanau violence1 It is research in progress towards a PhD, being carried out with the Ngati Hine Health Trust, in Te Taitokerau (Northland).

text: Mokopuna rising: Developing a best practice for early intervention in whanau violence In a very difficult meeting, an extremely unpleasant man was concerned about the possible loss of his power and the continuation of his plans and dreams for the future. And I said, yeah well, you should be worried, because according to your philosophy, when you’re dead, you’re dead. But when I’m dead, my mokopuna will be rising. (Personal Communication, Rob Cooper, 2007) 
“Mokopuna Rising” is about claiming space for Maori to define best practice for reducing and avoiding whanau violence1 It is research in progress towards a PhD, being carried out with the Ngati Hine Health Trust, in Te Taitokerau (Northland).

year: 2008

title: Learning Distance Metrics for Multi-Label Classification

authors: Gouk, Henry

subjects: Distance Metric Learning

abstract: Distance metric learning is a well studied problem in the field of machine learning, where it is typically used to improve the accuracy of instance based learning techniques. In this paper we propose a distance metric learning algorithm that is specialised for multi-label classification tasks, rather than the multiclass setting considered by most work in this area. The method trains an embedder that can transform instances into a feature space where squared Euclidean distance provides an estimate of the Jaccard distance between the corresponding label vectors. In addition to a linear Mahalanobis style metric, we also present a nonlinear extension that provides a substantial boost in performance. We show that this technique significantly improves upon current approaches for instance based multi-label classification, and also enables interesting data visualisations.

text: Learning Distance Metrics for Multi-Label Classification Distance metric learning is a well studied problem in the field of machine learning, where it is typically used to improve the accuracy of instance based learning techniques. In this paper we propose a distance metric learning algorithm that is specialised for multi-label classification tasks, rather than the multiclass setting considered by most work in this area. The method trains an embedder that can transform instances into a feature space where squared Euclidean distance provides an estimate of the Jaccard distance between the corresponding label vectors. In addition to a linear Mahalanobis style metric, we also present a nonlinear extension that provides a substantial boost in performance. We show that this technique significantly improves upon current approaches for instance based multi-label classification, and also enables interesting data visualisations.

year: 2016