title: Perceiving Necessity

authors: Legg, Catherine

abstract: There are many examples of diagrams in which one seems to perceive necessity – one sees not only that something is so, but that it must be so. That conflicts with some well-known philosophical theses, inherited from Hume, according to which there cannot be any “necessary connections between distinct existences” to be perceived; and even if there were, perception would not be capable of gaining access to them. We defend the perception of necessity, and explain why Hume fails to show its impossibility.

text: Perceiving Necessity There are many examples of diagrams in which one seems to perceive necessity – one sees not only that something is so, but that it must be so. That conflicts with some well-known philosophical theses, inherited from Hume, according to which there cannot be any “necessary connections between distinct existences” to be perceived; and even if there were, perception would not be capable of gaining access to them. We defend the perception of necessity, and explain why Hume fails to show its impossibility.

year: 2012-12

title: Benchmarking attribute selection techniques for discrete class data mining

authors: Hall, Mark A.

subjects: computer science

abstract: Data engineering is generally considered to be a central issue in the development of data mining applications.  The success of many learning schemes, in their attempts to construct models of data, hinges on the reliable identification of a small set of highly predictive attributes.  The inclusion of irrelevant, redundant and noisy attributes in the model building process phase can result in poor predictive performance and increased computation. 
Attribute selection generally involves a combination of search and attribute utility estimation plus evaluation with respect to specific learning schemes.  This leads to a large number of possible permutation and has led to a situation where very few benchmark studies have been conducted.
This paper presents a benchmark comparison of several attribute selection methods for supervised classification.  All the methods produce an attribute ranking, a useful devise for isolating the individual merit of an attribute. Attribute selection is achieved by cross-validating the attribute rankings with respect to a classification learner to find the best attributes.  Results are reported for a selection of standard data sets and two diverse learning schemes C4.5 and naïve Bayes.

text: Benchmarking attribute selection techniques for discrete class data mining Data engineering is generally considered to be a central issue in the development of data mining applications.  The success of many learning schemes, in their attempts to construct models of data, hinges on the reliable identification of a small set of highly predictive attributes.  The inclusion of irrelevant, redundant and noisy attributes in the model building process phase can result in poor predictive performance and increased computation. 
Attribute selection generally involves a combination of search and attribute utility estimation plus evaluation with respect to specific learning schemes.  This leads to a large number of possible permutation and has led to a situation where very few benchmark studies have been conducted.
This paper presents a benchmark comparison of several attribute selection methods for supervised classification.  All the methods produce an attribute ranking, a useful devise for isolating the individual merit of an attribute. Attribute selection is achieved by cross-validating the attribute rankings with respect to a classification learner to find the best attributes.  Results are reported for a selection of standard data sets and two diverse learning schemes C4.5 and naïve Bayes.

year: 2002-04

title: Te hau mihi ata mātauranga Māori and science - Cross cultural dialogue

authors: Joseph, Robert

abstract: The Te Hau Mihi Ata Mātauranga Māori and Science research project (the research project) will explore ways to link mātauranga Māori and western science. The purpose of the research project is to open a new creative knowledge space to unleash innovative thinking and cross-cultural connection that is unique to New Zealand which can be a source of untapped economic, social, environmental and cultural potential. The historic marginalisation of alternative value and knowledge systems has prevented quality engagement and dialogue between these two knowledge systems in New Zealand. The research project will develop, inter alia, a new inter-cultural dialogue process between Māori educated in western-based science and experts in mātauranga Māori.

text: Te hau mihi ata mātauranga Māori and science - Cross cultural dialogue The Te Hau Mihi Ata Mātauranga Māori and Science research project (the research project) will explore ways to link mātauranga Māori and western science. The purpose of the research project is to open a new creative knowledge space to unleash innovative thinking and cross-cultural connection that is unique to New Zealand which can be a source of untapped economic, social, environmental and cultural potential. The historic marginalisation of alternative value and knowledge systems has prevented quality engagement and dialogue between these two knowledge systems in New Zealand. The research project will develop, inter alia, a new inter-cultural dialogue process between Māori educated in western-based science and experts in mātauranga Māori.

year: 2008-11