Machine learning algorithms for event detection

Autor: Denver Dash, Dragos D. Margineantu, Weng-Keen Wong
Rok vydání: 2010
Předmět:
Zdroj: Machine Learning. 79:257-259
ISSN: 1573-0565
0885-6125
DOI: 10.1007/s10994-010-5184-9
Popis: A common task in many machine learning application domains involves monitoring routinely collected data for ‘interesting’ events. This task is prevalent in surveillance, but also in tasks ranging from the analysis of scientific data to the monitoring of naturally occurring events, and from supervising industrial processes to observing human behavior. We will refer to this monitoring process with the purpose of identifying interesting occurrences, as event detection. We put together this special issue of the Machine Learning journal with the belief that principled machine learning approaches can and will be a differentiator in addressing event detection tasks, and that theoretical and practical advances of machine learning in this area have the potential to impact a wide range of important real-world applications such as security, public health and medicine, biology, environmental sciences, manufacturing, astrophysics, business, and economics. In the recent past, domain experts in these areas have had the laborious job of manually examining the collected data for events of interest. With the emergence of computers, many efforts have been made to replace manual inspection with an automated process. Data, however, have become increasingly complex, and the quantities of collected data have become extremely large in recent years. Multivariate records, images, video footage, audio recordings, spatial and spatio-temporal data, text documents, and even relational data are now routinely collected. We all expect that advances in machine learning would be well-suited for this class of tasks. However, in practice, the peculiarities of the application often grossly violate the
Databáze: OpenAIRE
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