Classification, Novelty Detection and Clustering for Point Pattern Data
MetadataShow full item record
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 three fundamental machine learning problems, namely classification, novelty detection, and clustering, for point pattern data using two approaches: one with knowledge of the underlying data model (model-based approach), and one without (distance-based approach).
Showing items related by title, author, creator and subject.
Vo, Ba-Ngu; Dam, N.; Phung, D.; Tran, Q.; Vo, B. (2018)© 2018 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 ...
Kent, Peter; Kongsted, A. (2012)Background: Recently, there has been interest in using the short message service (SMS or text messaging), to gather frequent information on the clinical course of individual patients. One possible role for identifying ...
Modelling the co-occurence of Streptococcus pneumoniae with other bacterial and viral pathogens in the upper respiratory tractJacoby, P.; Watson, K.; Bowman, J.; Taylor, A.; Riley, T.; Smith, D.; Lehmann, Deborah (2007)Go to ScienceDirect® Home Skip Main Navigation Links Brought to you by: The University of Western Australia Library Login: + Register Athens/Institution Login Not Registered? - User Name: Password: ...