Multiple Learner Systems Using Resampling Methods
Date of Award
Master of Science (MS)
Nageswara S. V. Rao
Call Number for Print
Special Collections LD4331.C65X54
The N-Learners Problem deals with combining a number of learners such that the resultant system is "better", under some criterion, than the best of the individual learners. We consider a system of probably approximately correct concept learners. Depending on the available information, there are several methods to make the composite system better than the best of the individual learners. If a sample and an oracle that generates data points (but, not their classification) is available, then we show that we can achieve arbitrary levels of the normalized confidence of the composite system if (a) a robust learning algorithm is available, and (b) the hypothesis space of the fuser is "sufficiently rich". Our method consists of generating a suitable number of pseudo examples, and then performing another step of learning using the sample and the pseudo examples. This method, by restriction, can be used to boost the performance of a single PAC learner. Pseudo examples are generated by producing data points using the oracle and classifying them using the composite system.
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"Multiple Learner Systems Using Resampling Methods"
(1992). Master of Science (MS), Thesis, Computer Science, Old Dominion University, DOI: 10.25777/vmpf-3698