title: Applying propositional learning algorithms to multi-instance data

authors: Frank, Eibe

subjects: computer science

abstract: Multi-instance learning is commonly tackled using special-purpose algorithms. Development of these algorithms has started because early experiments with standard propositional learners have failed to produce satisfactory results on multi-instance data—more specifically, the Musk data. In this paper we present evidence that this is not necessarily the case. We introduce a simple wrapper for applying standard propositional learners to multi-instance problems and present empirical results for the Musk data that are competitive with genuine multi-instance algorithms. The key features of our new wrapper technique are: (1) it discards the standard multi-instance assumption that there is some inherent difference between positive and negative bags, and (2) it introduces weights to treat instances from different bags differently. We show that these two modifications are essential for producing good results on the Musk benchmark datasets.

text: Applying propositional learning algorithms to multi-instance data Multi-instance learning is commonly tackled using special-purpose algorithms. Development of these algorithms has started because early experiments with standard propositional learners have failed to produce satisfactory results on multi-instance data—more specifically, the Musk data. In this paper we present evidence that this is not necessarily the case. We introduce a simple wrapper for applying standard propositional learners to multi-instance problems and present empirical results for the Musk data that are competitive with genuine multi-instance algorithms. The key features of our new wrapper technique are: (1) it discards the standard multi-instance assumption that there is some inherent difference between positive and negative bags, and (2) it introduces weights to treat instances from different bags differently. We show that these two modifications are essential for producing good results on the Musk benchmark datasets.

year: 2003-06

title: The relevance of the ‘h’ and ‘g’ index to economics in the context of a nation-wide research evaluation scheme: The New Zealand case

authors: Anderson, David L.

subjects: g and h indexes

abstract: The purpose of this paper is to explore the relevance of the citation-based ‘h’ and ‘g’ indexes as a means for measuring research output in economics. This study is unique in that it is the first to utilize the ‘h’ and ‘g’ indexes in the context of a time limited evaluation period and to provide comprehensive coverage of all academic economists in all university-based economics departments within a nation state. For illustration purposes we have selected New Zealand’s Performance Based Research Fund (PBRF) as our evaluation scheme. In order to provide a frame of reference for ‘h’ and ‘g’ index output measures, we have also estimated research output using a number of journal-based weighting schemes. In general, our findings suggest that ‘h’ and ‘g’ index scores are strongly associated with low-powered journal ranking schemes and weakly associated with high powered journal weighting schemes. More specifically, we found the ‘h’ and ‘g’ indexes to suffer from a lack of differentiation: for example, 52 percent of all participants received a score of zero under both measures, and 92 and 89 percent received scores of two or less under ‘h’ and ‘g’, respectively. Overall, our findings suggest that ‘h’ and ‘g’ indexes should not be incorporated into a PBRF-like framework.

text: The relevance of the ‘h’ and ‘g’ index to economics in the context of a nation-wide research evaluation scheme: The New Zealand case The purpose of this paper is to explore the relevance of the citation-based ‘h’ and ‘g’ indexes as a means for measuring research output in economics. This study is unique in that it is the first to utilize the ‘h’ and ‘g’ indexes in the context of a time limited evaluation period and to provide comprehensive coverage of all academic economists in all university-based economics departments within a nation state. For illustration purposes we have selected New Zealand’s Performance Based Research Fund (PBRF) as our evaluation scheme. In order to provide a frame of reference for ‘h’ and ‘g’ index output measures, we have also estimated research output using a number of journal-based weighting schemes. In general, our findings suggest that ‘h’ and ‘g’ index scores are strongly associated with low-powered journal ranking schemes and weakly associated with high powered journal weighting schemes. More specifically, we found the ‘h’ and ‘g’ indexes to suffer from a lack of differentiation: for example, 52 percent of all participants received a score of zero under both measures, and 92 and 89 percent received scores of two or less under ‘h’ and ‘g’, respectively. Overall, our findings suggest that ‘h’ and ‘g’ indexes should not be incorporated into a PBRF-like framework.

year: 2012-04

title: Maori & Psychology Research Unit annual report 2007

authors: Rua, Mohi

subjects: Maori and Psychology Research Unit

abstract: Annual report of the Maori and Psychology Research Unit (MPRU) 2007. The unit was established in August of 1997. The unit is designed to provide a catalyst and support network for enhancing research concerning the psychological needs, aspirations, and priorities of Maori people. The MPRU is well situated to draw together skilled and experienced interdisciplinary research groups by networking and establishing working relationships with staff and students within the Faculty of Arts and Social Sciences, the University, and the wider community.

text: Maori & Psychology Research Unit annual report 2007 Annual report of the Maori and Psychology Research Unit (MPRU) 2007. The unit was established in August of 1997. The unit is designed to provide a catalyst and support network for enhancing research concerning the psychological needs, aspirations, and priorities of Maori people. The MPRU is well situated to draw together skilled and experienced interdisciplinary research groups by networking and establishing working relationships with staff and students within the Faculty of Arts and Social Sciences, the University, and the wider community.

year: 2008