- Tytuł:
- How to keep the HG weights non-negative : the truncated Perceptron reweighing rule
- Autorzy:
- Magri, G.
- Powiązania:
- https://bibliotekanauki.pl/articles/103939.pdf
- Data publikacji:
- 2015
- Wydawca:
- Polska Akademia Nauk. Instytut Podstaw Informatyki PAN
- Tematy:
-
Harmonic Grammar
error-driven learning
Perceptron
convergence - Opis:
- The literature on error-driven learning in Harmonic Grammar (HG) has adopted the Perceptron reweighing rule. Yet, this rule is not suited to HG, as it fails at ensuring non-negative weights. A variant is thus considered which truncates the updates at zero, keeping the weights non-negative. Convergence guarantees and error bounds for the original Perceptron are shown to extend to its truncated variant.
- Źródło:
-
Journal of Language Modelling; 2015, 3, 2; 345-375
2299-856X
2299-8470 - Pojawia się w:
- Journal of Language Modelling
- Dostawca treści:
- Biblioteka Nauki
Artykuł