title: Connecting with tephras: principles, functioning, and applications of tephrochronology in Quaternary science

authors: Lowe, David J.

abstract: Tephrochronology is a unique method for linking and dating geological, palaeoecological, palaeoclimatic, or archaeological sequences or events. The method relies firstly on stratigraphy and the law of superposition, which apply in any study that connects or correlates deposits from one place to another. Secondly, it relies on characterising and hence identifying or ‘fingerprinting’ tephra layers using either physical properties evident in the field or those obtained from laboratory analysis, including mineralogical examination by optical microscopy or geochemical analysis of glass shards or crystals (e.g., Fe-Ti oxides, ferromagnesian minerals) using the electron microprobe and other tools. Thirdly, the method is enhanced when a numerical age is obtained for a tephra layer by (1) radiometric methods such as radiocarbon, fission-track, U-series, or Ar/Ar dating, (2) incremental dating methods including dendrochronology or varved sediments or layering in ice cores, or (3) age-equivalent methods such as palaeomagnetism or correlation with marine oxygen isotope stages or palynostratigraphy. Once known, that age can be transferred from one site to the next using stratigraphic methods and by matching compositional characteristics, i.e., comparing ‘fingerprints’ from each layer. Used this way, tephrochronology is an age-equivalent dating method.

text: Connecting with tephras: principles, functioning, and applications of tephrochronology in Quaternary science Tephrochronology is a unique method for linking and dating geological, palaeoecological, palaeoclimatic, or archaeological sequences or events. The method relies firstly on stratigraphy and the law of superposition, which apply in any study that connects or correlates deposits from one place to another. Secondly, it relies on characterising and hence identifying or ‘fingerprinting’ tephra layers using either physical properties evident in the field or those obtained from laboratory analysis, including mineralogical examination by optical microscopy or geochemical analysis of glass shards or crystals (e.g., Fe-Ti oxides, ferromagnesian minerals) using the electron microprobe and other tools. Thirdly, the method is enhanced when a numerical age is obtained for a tephra layer by (1) radiometric methods such as radiocarbon, fission-track, U-series, or Ar/Ar dating, (2) incremental dating methods including dendrochronology or varved sediments or layering in ice cores, or (3) age-equivalent methods such as palaeomagnetism or correlation with marine oxygen isotope stages or palynostratigraphy. Once known, that age can be transferred from one site to the next using stratigraphic methods and by matching compositional characteristics, i.e., comparing ‘fingerprints’ from each layer. Used this way, tephrochronology is an age-equivalent dating method.

year: 2013

title: Pruning decision trees and lists

authors: Frank, Eibe

abstract: Machine learning algorithms are techniques that automatically build models describing the structure at the heart of a set of data. Ideally, such models can be used to predict properties of future data points and people can use them to analyze the domain from which the data originates. 

Decision trees and lists are potentially powerful predictors and embody an explicit representation of the structure in a dataset. Their accuracy and comprehensibility depends on how concisely the learning algorithm can summarize this structure. The final model should not incorporate spurious effects-patterns that are not genuine features of the underlying domain. Given an efficient mechanism for determining when a particular effect is due to chance alone, non-predictive parts of a model can be eliminated or “pruned.” Pruning mechanisms require a sensitive instrument that uses the data to detect whether there is a genuine relationship between the components of a model and the domain. Statistical significance tests are theoretically well-founded tools for doing exactly that. 

This thesis presents pruning algorithms for decision trees and lists that are based on significance tests. We explain why pruning is often necessary to obtain small and accurate models and show that the performance of standard pruning algorithms can be improved by taking the statistical significance of observations into account. We compare the effect of parametric and non-parametric tests, analyze why current pruning algorithms for decision lists often prune too aggressively, and review related work-in particular existing approaches that use significance tests in the context of pruning. The main outcome of this investigation is a set of simple pruning algorithms that should prove useful in practical data mining applications.

text: Pruning decision trees and lists Machine learning algorithms are techniques that automatically build models describing the structure at the heart of a set of data. Ideally, such models can be used to predict properties of future data points and people can use them to analyze the domain from which the data originates. 

Decision trees and lists are potentially powerful predictors and embody an explicit representation of the structure in a dataset. Their accuracy and comprehensibility depends on how concisely the learning algorithm can summarize this structure. The final model should not incorporate spurious effects-patterns that are not genuine features of the underlying domain. Given an efficient mechanism for determining when a particular effect is due to chance alone, non-predictive parts of a model can be eliminated or “pruned.” Pruning mechanisms require a sensitive instrument that uses the data to detect whether there is a genuine relationship between the components of a model and the domain. Statistical significance tests are theoretically well-founded tools for doing exactly that. 

This thesis presents pruning algorithms for decision trees and lists that are based on significance tests. We explain why pruning is often necessary to obtain small and accurate models and show that the performance of standard pruning algorithms can be improved by taking the statistical significance of observations into account. We compare the effect of parametric and non-parametric tests, analyze why current pruning algorithms for decision lists often prune too aggressively, and review related work-in particular existing approaches that use significance tests in the context of pruning. The main outcome of this investigation is a set of simple pruning algorithms that should prove useful in practical data mining applications.

year: 2000

title: It's harder in my language, but I still choose it

authors: Keegan, Te Taka Adrian Gregory

abstract: In New Zealand, the language that dominates most technologies is English. For a variety of reasons, interfaces have been made available in the Maori language for a range of modem technological tools. We have conducted usability studies on many of these products and fluent Maori users have almost unanimously stated that the products are more difficult and time consuming to use in the indigenous (Maori) language. They allude to unfamiliar instances and uses of Maori words, and to an ingrained familiarity with the English-language versions of these technologies, saying they would revert to the English-language interfaces if time is a factor. However, they also express a sense of pride at being able to see and use the Maori language in these various forms of media and state that although they experience some difficulty, the Maori-language versions would still be their first preference.

text: It's harder in my language, but I still choose it In New Zealand, the language that dominates most technologies is English. For a variety of reasons, interfaces have been made available in the Maori language for a range of modem technological tools. We have conducted usability studies on many of these products and fluent Maori users have almost unanimously stated that the products are more difficult and time consuming to use in the indigenous (Maori) language. They allude to unfamiliar instances and uses of Maori words, and to an ingrained familiarity with the English-language versions of these technologies, saying they would revert to the English-language interfaces if time is a factor. However, they also express a sense of pride at being able to see and use the Maori language in these various forms of media and state that although they experience some difficulty, the Maori-language versions would still be their first preference.

year: 2014