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We introduce an approach for exploring eigenvector localization phenomena for a class of (unbounded) selfadjoint operators. More specifically, given a target region and a tolerance, the algorithm identifies candidate eigenpairs for which the eigenvector is expected to be localized in the target region to within that tolerance. Theoretical results, together with detailed numerical illustrations of them, are provided that support our algorithm. A partial realization of the algorithm is described and tested, providing a proof of concept for the approach.
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Constraint breeding during on-line incremental learning
An evolutionary algorithm for simultaneously inducing and weighting phonological constraints (the Winnow-Maxent Subtree Breeder) is described, analyzed, and illustrated. Implementing weights as sub-population sizes, reproduction with selection executes a new variant of Winnow (Littlestone1988), which is shown to converge. A flexible constraint schema, based on the same prosodic and autosegmental trees used in representations, is described, together with algorithms for mutation and recombination (mating). The algorithm is applied to explaining abrupt learning curves, and predicts an empirical connection between abruptness and language-particularity.
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- Award ID(s):
- 1651105
- PAR ID:
- 10107388
- Date Published:
- Journal Name:
- Proceedings of the Society for Computation in Linguistics
- Volume:
- 2
- Page Range / eLocation ID:
- Article #9
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.7275/6f9x-6411
