Agnostic Learning of Geometric Patterns
1998
- 132Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage132
- Downloads128
- Abstract Views4
Report Description
Goldberg, Goldman, and Scott demonstrated how the problem of recognizing a landmark from a one-dimensional visual image can be mapped to that of learning a one-dimensional geometric pattern and gave a PAC algorithm to learn that class. In this paper, we present an efficient on-line agnostic learning algorithm for learning the class of constant-dimension geometric patterns. Our algorithm can tolerate both classification and attribute noise. By working in higher dimensional spaces we can represent more features from the visual image in the geometric pattern. Our mapping of the data to a geometric pattern, and hence our learning algorithm, is applicable to any data representable as a constant-dimensional array of values, e.g. sonar data, temporal difference information, or amplitudes of a waveform. To our knowledge, these classes of patterns are more complex than any class of geometric patterns previously studied. Also, our results are easily adapted to learn the union of fixed-dimensional boxes from multiple-instance examples. Finally, our algorithms are tolerant of concept shift.
Bibliographic Details
https://openscholarship.wustl.edu/cse_research/475; https://openscholarship.wustl.edu/cgi/viewcontent.cgi?article=1475&context=cse_research; http://dx.doi.org/10.7936/k72v2dbp; https://doi.org/10.7936%2Fk72v2dbp; https://dx.doi.org/10.7936/k72v2dbp; https://openscholarship.wustl.edu/cse_research/475/
Washington University in St. Louis Libraries
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