title: Should physical activity be on the healthy ageing agenda?

authors: Grant, Bevan C.

subjects: physical activity

abstract: Physical activity makes a notable contribution to one’s independence, ability to perform everyday tasks and quality of life in the later years, but engagement in this health-related behaviour decreases with age. It is therefore not surprising that numerous intervention strategies have been applied in many settings to encourage a physically active lifestyle.
Nonetheless, these have little long-lasting effect. Knowing how best to promote such behaviour to people over 65 years old in a way that makes them feel I want to engage is still in its infancy. This paper reflects on how ageing influences the decisions people in later life make in relation to adopting a physically active lifestyle, and how any advocacy needs to look beyond the physical experience. Knowing of the potential benefits to be gained from this self-regulating behaviour means it warrants a more prominent place on the promoting healthy ageing agenda.

text: Should physical activity be on the healthy ageing agenda? Physical activity makes a notable contribution to one’s independence, ability to perform everyday tasks and quality of life in the later years, but engagement in this health-related behaviour decreases with age. It is therefore not surprising that numerous intervention strategies have been applied in many settings to encourage a physically active lifestyle.
Nonetheless, these have little long-lasting effect. Knowing how best to promote such behaviour to people over 65 years old in a way that makes them feel I want to engage is still in its infancy. This paper reflects on how ageing influences the decisions people in later life make in relation to adopting a physically active lifestyle, and how any advocacy needs to look beyond the physical experience. Knowing of the potential benefits to be gained from this self-regulating behaviour means it warrants a more prominent place on the promoting healthy ageing agenda.

year: 2008-03

title: Scalable Multi-label Classification

authors: Read, Jesse

subjects: multi-label

abstract: Multi-label classification is relevant to many domains, such as text, image and other media, and bioinformatics. Researchers have already noticed that in multi-label data, correlations exist between labels, and a variety of approaches, drawing inspiration from many spheres of machine learning, have been able to model these correlations. However, data sources from the real world are growing ever larger and the multi-label task is particularly sensitive to this due to the complexity associated with multiple labels and the correlations between them. Consequently, many methods do not scale up to large problems. This thesis deals with scalable multi-label classification: methods which exhibit high predictive performance, but are also able to scale up to larger problems. The first major contribution is the pruned sets method, which is able to model label correlations directly for high predictive performance, but reduces overfitting and complexity over related methods by pruning and subsampling label sets, and can thus scale up to larger datasets. The second major contribution is the classifier chains method, which models correlations with a chain of binary classifiers. The use of binary models allows for scalability to even larger datasets. Pruned sets and classifier chains are robust with respect to both the variety and scale of data that they can deal with, and can be incorporated into other methods. In an ensemble scheme, these methods are able to compete with state-of-the-art methods in terms of predictive performance as well as scale up to large datasets of hundreds of thousands of training examples. This thesis also puts a special emphasis on multi-label evaluation; introducing a new evaluation measure and studying threshold calibration. With one of the largest and most varied collections of multi-label datasets in the literature, extensive experimental evaluation shows the advantage of these methods, both in terms of predictive performance, and computational efficiency and scalability.

text: Scalable Multi-label Classification Multi-label classification is relevant to many domains, such as text, image and other media, and bioinformatics. Researchers have already noticed that in multi-label data, correlations exist between labels, and a variety of approaches, drawing inspiration from many spheres of machine learning, have been able to model these correlations. However, data sources from the real world are growing ever larger and the multi-label task is particularly sensitive to this due to the complexity associated with multiple labels and the correlations between them. Consequently, many methods do not scale up to large problems. This thesis deals with scalable multi-label classification: methods which exhibit high predictive performance, but are also able to scale up to larger problems. The first major contribution is the pruned sets method, which is able to model label correlations directly for high predictive performance, but reduces overfitting and complexity over related methods by pruning and subsampling label sets, and can thus scale up to larger datasets. The second major contribution is the classifier chains method, which models correlations with a chain of binary classifiers. The use of binary models allows for scalability to even larger datasets. Pruned sets and classifier chains are robust with respect to both the variety and scale of data that they can deal with, and can be incorporated into other methods. In an ensemble scheme, these methods are able to compete with state-of-the-art methods in terms of predictive performance as well as scale up to large datasets of hundreds of thousands of training examples. This thesis also puts a special emphasis on multi-label evaluation; introducing a new evaluation measure and studying threshold calibration. With one of the largest and most varied collections of multi-label datasets in the literature, extensive experimental evaluation shows the advantage of these methods, both in terms of predictive performance, and computational efficiency and scalability.

year: 2010

title: Claiming Spaces: Proceedings of the 2007 National Maori and Pacific Psychologies Symposium 23rd-24th November 2007

authors: Levy, Michelle Patricia

subjects: Maori

abstract: This is the full conference proceedings of Claiming Spaces: Proceedings of the 2007 National Maori and Pacific Psychologies Symposium 23rd-24th November 2007.

text: Claiming Spaces: Proceedings of the 2007 National Maori and Pacific Psychologies Symposium 23rd-24th November 2007 This is the full conference proceedings of Claiming Spaces: Proceedings of the 2007 National Maori and Pacific Psychologies Symposium 23rd-24th November 2007.

year: 2008