title: Classification

authors: Witten, Ian H.

subjects: computer science

abstract: In Classification learning, an algorithm is presented with a set of classified examples or ‘‘instances’’ from which it is expected to infer a way of classifying unseen instances into one of several ‘‘classes’’. Instances have a set of features or ‘‘attributes’’ whose values define that particular instance. Numeric prediction, or ‘‘regression,’’ is a variant of classification learning in which the class attribute is numeric rather than categorical. Classification learning is sometimes called supervised because the method operates under supervision by being provided with the actual outcome for each of the training instances. This contrasts with Data clustering (see entry Data Clustering), where the classes are not given, and with Association learning (see entry Association Learning), which seeks any association – not just one that predicts the class.

text: Classification In Classification learning, an algorithm is presented with a set of classified examples or ‘‘instances’’ from which it is expected to infer a way of classifying unseen instances into one of several ‘‘classes’’. Instances have a set of features or ‘‘attributes’’ whose values define that particular instance. Numeric prediction, or ‘‘regression,’’ is a variant of classification learning in which the class attribute is numeric rather than categorical. Classification learning is sometimes called supervised because the method operates under supervision by being provided with the actual outcome for each of the training instances. This contrasts with Data clustering (see entry Data Clustering), where the classes are not given, and with Association learning (see entry Association Learning), which seeks any association – not just one that predicts the class.

year: 2009

title: Making paperless work

authors: Plimmer, Beryl

subjects: computer science

abstract: Despite well documented advantages, attempts to go truly "paperless" seldom succeed. This is principally because computer-based paperless systems typically do not support all of the affordances of paper, nor the work process that have evolved with paper-based systems. We suggest that attention to users' work environments, activities and practices are critical to the success of paperless systems. This paper describes the development and effective utilization of a software tool for the paperless marking of student assignments which does not require users to compromise on established best practice. It includes a significant advance in the task management support.

text: Making paperless work Despite well documented advantages, attempts to go truly "paperless" seldom succeed. This is principally because computer-based paperless systems typically do not support all of the affordances of paper, nor the work process that have evolved with paper-based systems. We suggest that attention to users' work environments, activities and practices are critical to the success of paperless systems. This paper describes the development and effective utilization of a software tool for the paperless marking of student assignments which does not require users to compromise on established best practice. It includes a significant advance in the task management support.

year: 2007

title: New Zealand regions, 1986 – 2001: Hospitalisation and some related health facts

authors: Pool, Ian

subjects: health

abstract: Once age and gender composition is controlled for, regional health differentials are a function of problems of health service delivery, of socio-economic variance, and overall Māori Pakeha health differences. They indicate relative levels of exclusion and of inequality. This paper shows that these differentials follow in general the patterns seen in other papers in this series.

text: New Zealand regions, 1986 – 2001: Hospitalisation and some related health facts Once age and gender composition is controlled for, regional health differentials are a function of problems of health service delivery, of socio-economic variance, and overall Māori Pakeha health differences. They indicate relative levels of exclusion and of inequality. This paper shows that these differentials follow in general the patterns seen in other papers in this series.

year: 2006-03