title: Constructing programs or processes

authors: Reeves, Steve

subjects: cause

abstract: We define interacting sequential programs, motivated originally by constructivist considerations. We use them to investigate notions of implementation and determinism. Process algebras do not define what can be implemented and what cannot. As we demonstrate it is problematic to do so on the set of all processes. Guided by constructivist notions we have constructed interacting sequential programs which we claim can be readily implemented and are a subset of processes.

text: Constructing programs or processes We define interacting sequential programs, motivated originally by constructivist considerations. We use them to investigate notions of implementation and determinism. Process algebras do not define what can be implemented and what cannot. As we demonstrate it is problematic to do so on the set of all processes. Guided by constructivist notions we have constructed interacting sequential programs which we claim can be readily implemented and are a subset of processes.

year: 2005-12-28

title: An entropy gain measure of numeric prediction performance

authors: Trigg, Leonard E.

subjects: computer science

abstract: Categorical classifier performance is typically evaluated with respect to error rate, expressed as a percentage of test instances that were not correctly classified. When a classifier produces multiple classifications for a test instance, the prediction is counted as incorrect (even if the correct class was one of the predictions). Although commonly used in the literature, error rate is a coarse measure of classifier performance, as it is based only on a single prediction offered for a test instance. Since many classifiers can produce a class distribution as a prediction, we should use this to provide a better measure of how much information the classifier is extracting from the domain. 
Numeric classifiers are a relatively new development in machine learning, and as such there is no single performance measure that has become standard. Typically these machine learning schemes predict a single real number for each test instance, and the error between the predicted and actual value is used to calculate a myriad of performance measures such as correlation coefficient, root mean squared error, mean absolute error, relative absolute error, and root relative squared error. With so many performance measures it is difficult to establish an overall performance evaluation.
The next section describes a performance measure for machine learning schemes that attempts to overcome the problems with current measures. In addition, the same evaluation measure is used for categorical and numeric classifier.

text: An entropy gain measure of numeric prediction performance Categorical classifier performance is typically evaluated with respect to error rate, expressed as a percentage of test instances that were not correctly classified. When a classifier produces multiple classifications for a test instance, the prediction is counted as incorrect (even if the correct class was one of the predictions). Although commonly used in the literature, error rate is a coarse measure of classifier performance, as it is based only on a single prediction offered for a test instance. Since many classifiers can produce a class distribution as a prediction, we should use this to provide a better measure of how much information the classifier is extracting from the domain. 
Numeric classifiers are a relatively new development in machine learning, and as such there is no single performance measure that has become standard. Typically these machine learning schemes predict a single real number for each test instance, and the error between the predicted and actual value is used to calculate a myriad of performance measures such as correlation coefficient, root mean squared error, mean absolute error, relative absolute error, and root relative squared error. With so many performance measures it is difficult to establish an overall performance evaluation.
The next section describes a performance measure for machine learning schemes that attempts to overcome the problems with current measures. In addition, the same evaluation measure is used for categorical and numeric classifier.

year: 1998-05

title: The art of dialogue with indigenous communities in the new biotechnology world

authors: Hudson, Maui

subjects: biotechnology

abstract: Te Hau Mihi Ata is a research project that aims to negotiate spaces for and develop processes of dialogue that allow for a deeper level of interaction between mtauranga Māori (Māori indigenous knowledge) and science. Over a two-year period a series of facilitated exchanges or wnanga were held focusing on areas of new technology (assisted reproductive technologies, life technologies, and future food technologies) that involved Māori scientists and people with expertise in mtauranga Māori. These dialogue events were designed to explore, through the challenge of considering new biotechnologies, the similarities and differences that emerge from approaching these issues from different knowledge paradigms. This paper will outline the key project findings from this series of exchanges including the barriers and facilitators to the dialogue process and knowledge exchange as well as discuss how participants created connections between scientific discourse and indigenous knowledge frameworks.

text: The art of dialogue with indigenous communities in the new biotechnology world Te Hau Mihi Ata is a research project that aims to negotiate spaces for and develop processes of dialogue that allow for a deeper level of interaction between mtauranga Māori (Māori indigenous knowledge) and science. Over a two-year period a series of facilitated exchanges or wnanga were held focusing on areas of new technology (assisted reproductive technologies, life technologies, and future food technologies) that involved Māori scientists and people with expertise in mtauranga Māori. These dialogue events were designed to explore, through the challenge of considering new biotechnologies, the similarities and differences that emerge from approaching these issues from different knowledge paradigms. This paper will outline the key project findings from this series of exchanges including the barriers and facilitators to the dialogue process and knowledge exchange as well as discuss how participants created connections between scientific discourse and indigenous knowledge frameworks.

year: 2012