Model-based learning for point pattern data
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This article proposes a framework for model-based point pattern learning using point process theory. Likelihood functions for point pattern data derived from point process theory enable principled yet conceptually transparent extensions of learning tasks, such as classification, novelty detection and clustering, to point pattern data. Furthermore, tractable point pattern models as well as solutions for learning and decision making from point pattern data are developed.
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Tran, Quang Nhat (2017)Point pattern data, also known as multiple instance data or bags, are abundant in nature and applications. However, machine learning problems for point patterns have not received much attention. In this work, we solve ...
The impact of instructional interventions on students' learning approaches, attitudes, and achievement.Edwards, Peta S. (1999)Many interacting factors need to be considered when contemplating the optimum conditions for the creation of a learning environment that is compatible with the aims of tertiary teaching and learning. In the current economic ...
Vo, Ba Tuong; Tran, N.; Phung, D.; Vo, Ba-Ngu (2017)© 2016 IEEE. Point patterns are sets or multi-sets of unordered elements that can be found in numerous data sources. However, in data analysis tasks such as classification and novelty detection, appropriate statistical ...