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Full-Text Articles in Physical Sciences and Mathematics
Sgpm: Static Group Pattern Mining Using Apriori-Like Sliding Window, John Goh, David Taniar, Ee Peng Lim
Sgpm: Static Group Pattern Mining Using Apriori-Like Sliding Window, John Goh, David Taniar, Ee Peng Lim
Research Collection School Of Computing and Information Systems
Mobile user data mining is a field that focuses on extracting interesting pattern and knowledge out from data generated by mobile users. Group pattern is a type of mobile user data mining method. In group pattern mining, group patterns from a given user movement database is found based on spatio-temporal distances. In this paper, we propose an improvement of efficiency using area method for locating mobile users and using sliding window for static group pattern mining. This reduces the complexity of valid group pattern mining problem. We support the use of static method, which uses areas and sliding windows instead …
Corrective Feedback And Persistent Learning For Information Extraction, Aron Culotta, Trausti Kristjansson, Andrew Mccallum, Paul Viola
Corrective Feedback And Persistent Learning For Information Extraction, Aron Culotta, Trausti Kristjansson, Andrew Mccallum, Paul Viola
Andrew McCallum
To successfully embed statistical machine learning models in real world applications, two post-deployment capabilities must be provided: (1) the ability to solicit user corrections and (2) the ability to update the model from these corrections. We refer to the former capability as corrective feedback and the latter as persistent learning. While these capabilities have a natural implementation for simple classification tasks such as spam filtering, we argue that a more careful design is required for structured classification tasks. One example of a structured classification task is information extraction, in which raw text is analyzed to automatically populate a database. In …