title: Edward Cameron: a mine manager in the Tui Mines

authors: Hart, Philip

abstract: Edward (Ned) Cameron was an experienced miner before settling in New Zealand, where he worked on the Thames goldfield in many mines and becoming a mine manager. He also mined on other Hauraki fields, in particular Waiorongomai after 1881 and Tui after 1885. In both, he managed several mines and undertook a considerable amount of prospecting, anticipating good results that were not achieved (or achievable). As with others who worked in the Te Aroha district, his efforts were defeated by the low quality of the ore, and although he earned sufficient to make a satisfactory living, he did not prosper.

text: Edward Cameron: a mine manager in the Tui Mines Edward (Ned) Cameron was an experienced miner before settling in New Zealand, where he worked on the Thames goldfield in many mines and becoming a mine manager. He also mined on other Hauraki fields, in particular Waiorongomai after 1881 and Tui after 1885. In both, he managed several mines and undertook a considerable amount of prospecting, anticipating good results that were not achieved (or achievable). As with others who worked in the Te Aroha district, his efforts were defeated by the low quality of the ore, and although he earned sufficient to make a satisfactory living, he did not prosper.

year: 2016

title: Ensembles of restricted Hoeffding trees

authors: Bifet, Albert

subjects: design

abstract: The success of simple methods for classification shows that is is often not necessary to model complex attribute interactions to obtain good classification accuracy on practical problems. In this article, we propose to exploit this phenomenon in the data stream context by building an ensemble of Hoeffding trees that are each limited to a small subset of attributes. In this way, each tree is restricted to model interactions between attributes in its corresponding subset. Because it is not known a priori which attribute subsets are relevant for prediction, we build exhaustive ensembles that consider all possible attribute subsets of a given size. As the resulting Hoeffding trees are not all equally important, we weigh them in a suitable manner to obtain accurate classifications. This is done by combining the log-odds of their probability estimates using sigmoid perceptrons, with one perceptron per class. We propose a mechanism for setting the perceptrons' learning rate using the ADWIN change detection method for data streams, and also use ADWIN to reset ensemble members (i.e., Hoeffding trees) when they no longer perform well. Our experiments show that the resulting ensemble classifier outperforms bagging for data streams in terms of accuracy when both are used in conjunction with adaptive naive Bayes Hoeffding trees, at the expense of runtime and memory consumption. We also show that our stacking method can improve the performance of a bagged ensemble.

text: Ensembles of restricted Hoeffding trees The success of simple methods for classification shows that is is often not necessary to model complex attribute interactions to obtain good classification accuracy on practical problems. In this article, we propose to exploit this phenomenon in the data stream context by building an ensemble of Hoeffding trees that are each limited to a small subset of attributes. In this way, each tree is restricted to model interactions between attributes in its corresponding subset. Because it is not known a priori which attribute subsets are relevant for prediction, we build exhaustive ensembles that consider all possible attribute subsets of a given size. As the resulting Hoeffding trees are not all equally important, we weigh them in a suitable manner to obtain accurate classifications. This is done by combining the log-odds of their probability estimates using sigmoid perceptrons, with one perceptron per class. We propose a mechanism for setting the perceptrons' learning rate using the ADWIN change detection method for data streams, and also use ADWIN to reset ensemble members (i.e., Hoeffding trees) when they no longer perform well. Our experiments show that the resulting ensemble classifier outperforms bagging for data streams in terms of accuracy when both are used in conjunction with adaptive naive Bayes Hoeffding trees, at the expense of runtime and memory consumption. We also show that our stacking method can improve the performance of a bagged ensemble.

year: 2012

title: The Control, environmental sustainability and information management of Maori land in New Zealand

authors: Morad, Munir

subjects: Maori land

abstract: An increase in Maori land claims and the subsequent settlement of a number of land confiscation grievances have prompted calls for change to the land management, cadastral and legal regimes in New Zealand. There is little expert agreement as to Me shape any such reforms should take, but environmental planners and Maori leaders have conceded the need for an overhaul of current land information management practices. Such reforms must aspire to being legally workable, culturally appropriate and environmentally sustainable.
Most land in New Zealand is subject to recent legislation (post-1984) which acknowledges concern for Maori cultural values and ancestral rights and environmental sustainability. The existence of this legislation and growing Maori involvement in formulating public policy for the management of land and environmental resources mean that Maori concepts of land management and sustainability are receiving increasing attention and are likely to influence the shape of future cadastral reforms for Maori lands.

text: The Control, environmental sustainability and information management of Maori land in New Zealand An increase in Maori land claims and the subsequent settlement of a number of land confiscation grievances have prompted calls for change to the land management, cadastral and legal regimes in New Zealand. There is little expert agreement as to Me shape any such reforms should take, but environmental planners and Maori leaders have conceded the need for an overhaul of current land information management practices. Such reforms must aspire to being legally workable, culturally appropriate and environmentally sustainable.
Most land in New Zealand is subject to recent legislation (post-1984) which acknowledges concern for Maori cultural values and ancestral rights and environmental sustainability. The existence of this legislation and growing Maori involvement in formulating public policy for the management of land and environmental resources mean that Maori concepts of land management and sustainability are receiving increasing attention and are likely to influence the shape of future cadastral reforms for Maori lands.

year: 1997