Technical Report CS-2013-06

Title: Resource-Bounded Selection of Learning Algorithms
Authors: Assaf Glazer and Shaul Markovitch
Abstract: We present a new setup and a proposed solution for the resource-bounded learning-algorithm selection problem. Our setup assumes that a resource allocation pattern in a meta-learning system can be predefined. We use these predefined patterns to design novel preference utility functions between pairs of learning algorithms. These functions are exploited by a meta-learning process to support the selection of the best estimated learning algorithm when resources are limited. The resource allocation pattern is formulated as a distribution over the allocated execution time, and it may be considered more natural or more convenient in many real-life learning systems in which the pattern can be estimated or refined from previously solved learning tasks. In addition, we introduce a new evaluation methodology that uses an interruptible anytime algorithm to evaluate ranking method performance. This evaluation methodology, combined with a distribution over the allocated execution time, allows us to measure the ranking method's contribution in terms of its expected estimated error. Finally, we introduce a general anytime learning-algorithm selection framework for operating in a resource-bounded environment. Our framework can be used, for example, in a meta-learning advisory system or in an automated centralized server dealing with thousands of incoming learning tasks.
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