title: Stacking bagged and dagged models

authors: Ting, Kai Ming

subjects: bagging

abstract: In this paper, we investigate the method of stacked generalization in combining models derived from different subsets of a training dataset by a single learning algorithm, as well as different algorithms. The simplest way to combine predictions from competing models is majority vote, and the effect of the sampling regime used to generate training subsets has already been studied in this context-when bootstrap samples are used the method is called bagging, and for disjoint samples we call it dagging. This paper extends these studies to stacked generalization, where a learning algorithm is employed to combine the models. This yields new methods dubbed bag-stacking and dag-stacking. 
We demonstrate that bag-stacking and dag-stacking can be effective for classification tasks even when the training samples cover just a small fraction of the full dataset. In contrast to earlier bagging results, we show that bagging and bag-stacking work for stable as well as unstable learning algorithms, as do dagging and dag-stacking. We find that bag-stacking (dag-stacking) almost always has higher predictive accuracy than bagging (dagging), and we also show that bag-stacking models derived using two different algorithms is more effective than conventional bagging.

text: Stacking bagged and dagged models In this paper, we investigate the method of stacked generalization in combining models derived from different subsets of a training dataset by a single learning algorithm, as well as different algorithms. The simplest way to combine predictions from competing models is majority vote, and the effect of the sampling regime used to generate training subsets has already been studied in this context-when bootstrap samples are used the method is called bagging, and for disjoint samples we call it dagging. This paper extends these studies to stacked generalization, where a learning algorithm is employed to combine the models. This yields new methods dubbed bag-stacking and dag-stacking. 
We demonstrate that bag-stacking and dag-stacking can be effective for classification tasks even when the training samples cover just a small fraction of the full dataset. In contrast to earlier bagging results, we show that bagging and bag-stacking work for stable as well as unstable learning algorithms, as do dagging and dag-stacking. We find that bag-stacking (dag-stacking) almost always has higher predictive accuracy than bagging (dagging), and we also show that bag-stacking models derived using two different algorithms is more effective than conventional bagging.

year: 1997-03

title: Christian discipleship and interfaith engagement

authors: Douglas, Pratt

subjects: Christians

abstract: Ever since the famous 1910 Edinburgh World Mission conference Christian individuals and the Christian Church have been increasingly challenged to relate in new ways to people of other faiths. Reflecting on the relationship between Christian discipleship and interfaith engagement this article addresses three questions. Can a biblical basis for such engagement be discerned? What is the impact of the "Great Commission" at the end of Matthew's gospel (28:18-20)? How might a new understanding of mission and discipleship relate to concerns about interreligious dialogue? In other words, can Christian discipleship actively enable positive interfaith relations and engagement with adherents of other faiths? In conclusion, the article points to a number of considerations that might indeed contribute to just such an understanding of discipleship.

text: Christian discipleship and interfaith engagement Ever since the famous 1910 Edinburgh World Mission conference Christian individuals and the Christian Church have been increasingly challenged to relate in new ways to people of other faiths. Reflecting on the relationship between Christian discipleship and interfaith engagement this article addresses three questions. Can a biblical basis for such engagement be discerned? What is the impact of the "Great Commission" at the end of Matthew's gospel (28:18-20)? How might a new understanding of mission and discipleship relate to concerns about interreligious dialogue? In other words, can Christian discipleship actively enable positive interfaith relations and engagement with adherents of other faiths? In conclusion, the article points to a number of considerations that might indeed contribute to just such an understanding of discipleship.

year: 2009

title: Niupepa: A historical newspaper collection

authors: Apperley, Mark

abstract: Niupepa is a collection of 42 newspaper titles published in New Zealand from 1842-1933, comprising a total of 21,000 pages in 1,750 issues. This collection forms a unique historical record of the language of the indigenous Mãori people, the evolution of the written form of this language, and of events and developments during the formative colonial history of our country. Using the Greenstone software from the New Zealand Digital Library this collection is now publicly available with full-text search capability.

text: Niupepa: A historical newspaper collection Niupepa is a collection of 42 newspaper titles published in New Zealand from 1842-1933, comprising a total of 21,000 pages in 1,750 issues. This collection forms a unique historical record of the language of the indigenous Mãori people, the evolution of the written form of this language, and of events and developments during the formative colonial history of our country. Using the Greenstone software from the New Zealand Digital Library this collection is now publicly available with full-text search capability.

year: 2001-05-01