title: Improving on bagging with input smearing

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: Improving on bagging with input smearing 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: 2006

title: Social music in cars

authors: Cunningham, Sally Jo

abstract: This paper builds an understanding of how music is currently experienced by a social group travelling together in a car - how songs are chosen for playing, how music both reflects and influences the group’s mood and social interaction, who supplies the music, the hardware/software that supports song selection and presentation. This fine-grained context emerges from a qualitative analysis of a rich set of ethnographic data (participant observations and interviews) focusing primarily on the experience of in-car music on moderate length and long trips. We suggest features and functionality for music software to enhance the social experience when travelling in cars, and prototype and test a user interface based on design suggestions drawn from the data.

text: Social music in cars This paper builds an understanding of how music is currently experienced by a social group travelling together in a car - how songs are chosen for playing, how music both reflects and influences the group’s mood and social interaction, who supplies the music, the hardware/software that supports song selection and presentation. This fine-grained context emerges from a qualitative analysis of a rich set of ethnographic data (participant observations and interviews) focusing primarily on the experience of in-car music on moderate length and long trips. We suggest features and functionality for music software to enhance the social experience when travelling in cars, and prototype and test a user interface based on design suggestions drawn from the data.

year: 2014

title: The Aroha block from 1880 onwards

authors: Hart, Philip

abstract: With the discovery of gold in 1880 and the pressure to open the land for mining, there was a need to determine the boundaries of the Ngati Rahiri reserves and to subdivide these amongst the owners. As well, terms for paying goldfields revenue had to be negotiated before the goldfield could be opened, and after its opening the claims of Maori with little or no basis for their claims of ownership had to be investigated. Wairakau reserve, outside the goldfield, was not subdivided until mid-1882, and arguments over the ownership of Tui Pa continued well into the twentieth century. 
At first, sales of Crown land found few takers, especially because the farm sections required draining before they could be developed. Ngati Rahiri reserves were designated as being inalienable, but they could be leased for 21 years, as some portions were, at low rates. Very quickly, officials, who feared Maori would become landless, had to fend off requests to remove restrictions on sale made by owners who in some instances lived far from Te Aroha and who all wanted to obtain money from selling land of little use to them. The indebtedness of some owners led to continued pressure to sell, though often the money they received was wasted. With the fading of the goldfield, miners sought land for farms, and were frustrated at the Crown not purchasing the Ngati Rahiri reserves. 
Land was acquired by the Crown to extend the hot springs domain, and after negotiating improved leases in the Te Aroha township the freehold of this settlement was acquired. Some Maori retained their land, which Pakeha accused them of not improving; Maori requests that the government provide training in farming methods were ignored. By the twentieth century most Ngati Rahiri had become landless, but as many examples illustrate, this was commonly because cash-strapped owners insisted on selling their interests for an immediate financial return.

text: The Aroha block from 1880 onwards With the discovery of gold in 1880 and the pressure to open the land for mining, there was a need to determine the boundaries of the Ngati Rahiri reserves and to subdivide these amongst the owners. As well, terms for paying goldfields revenue had to be negotiated before the goldfield could be opened, and after its opening the claims of Maori with little or no basis for their claims of ownership had to be investigated. Wairakau reserve, outside the goldfield, was not subdivided until mid-1882, and arguments over the ownership of Tui Pa continued well into the twentieth century. 
At first, sales of Crown land found few takers, especially because the farm sections required draining before they could be developed. Ngati Rahiri reserves were designated as being inalienable, but they could be leased for 21 years, as some portions were, at low rates. Very quickly, officials, who feared Maori would become landless, had to fend off requests to remove restrictions on sale made by owners who in some instances lived far from Te Aroha and who all wanted to obtain money from selling land of little use to them. The indebtedness of some owners led to continued pressure to sell, though often the money they received was wasted. With the fading of the goldfield, miners sought land for farms, and were frustrated at the Crown not purchasing the Ngati Rahiri reserves. 
Land was acquired by the Crown to extend the hot springs domain, and after negotiating improved leases in the Te Aroha township the freehold of this settlement was acquired. Some Maori retained their land, which Pakeha accused them of not improving; Maori requests that the government provide training in farming methods were ignored. By the twentieth century most Ngati Rahiri had become landless, but as many examples illustrate, this was commonly because cash-strapped owners insisted on selling their interests for an immediate financial return.

year: 2016