Optimal online prediction in adversarial environments
Bartlett, Peter L. (2010) Optimal online prediction in adversarial environments. Algorithmic Learning Theory: 21st International Conference Proceedings [Lecture Notes in Computer Science], 6331, p. 34.
In many prediction problems, including those that arise in computer security and computational finance, the process generating the data is best modelled as an adversary with whom the predictor competes. Even decision problems that are not inherently adversarial can be usefully modeled in this way, since the assumptions are sufficiently weak that effective prediction strategies for adversarial settings are very widely applicable.
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|Item Type:||Journal Article|
|Keywords:||prediction problems, computer security, computational finance|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > COMPUTATION THEORY AND MATHEMATICS (080200)|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology|
Past > Schools > Mathematical Sciences
|Copyright Owner:||Copyright 2010 Springer|
|Deposited On:||18 Aug 2011 11:25|
|Last Modified:||02 Oct 2013 14:45|
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