Smoothing and compression with stochastic k-testable tree languages
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Title: | Smoothing and compression with stochastic k-testable tree languages |
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Authors: | Rico-Juan, Juan Ramón | Calera Rubio, Jorge | Carrasco, Rafael C. |
Research Group/s: | Transducens |
Center, Department or Service: | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos |
Keywords: | Tree grammars | Stochastic models | Ssmoothing | Backing-off | Data compression |
Knowledge Area: | Lenguajes y Sistemas Informáticos |
Issue Date: | 2-Apr-2005 |
Publisher: | Elsevier |
Citation: | RICO JUAN, Juan Ramón; CALERA RUBIO, Jorge; CARRASCO JIMÉNEZ, Rafael Carlos. "Smoothing and compression with stochastic k-testable tree languages". Pattern Recognition. Vol. 38, No. 9 (Sept. 2005). ISSN 0031-3203, pp. 1420-1430 |
Abstract: | In this paper, we describe some techniques to learn probabilistic k-testable tree models, a generalization of the well known k-gram models, that can be used to compress or classify structured data. These models are easy to infer from samples and allow for incremental updates. Moreover, as shown here, backing-off schemes can be defined to solve data sparseness, a problem that often arises when using trees to represent the data. These features make them suitable to compress structured data files at a better rate than string-based methods. |
Sponsor: | The Spanish Comisión Interministerial de Ciencia y Tecnología through Grants TIC2003-08681-C02 and TIC2003-08496-C04. |
URI: | http://hdl.handle.net/10045/14022 |
ISSN: | 0031-3203 (Print) | 1873-5142 (Online) |
DOI: | 10.1016/j.patcog.2004.03.024 |
Language: | eng |
Type: | info:eu-repo/semantics/article |
Peer Review: | si |
Publisher version: | http://dx.doi.org/10.1016/j.patcog.2004.03.024 |
Appears in Collections: | INV - TRANSDUCENS - Artículos de Revistas |
Files in This Item:
File | Description | Size | Format | |
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pr04.pdf | Preprint (acceso libre) | 215,85 kB | Adobe PDF | Open Preview |
smoothing_compression.pdf | Versión final (acceso restringido) | 393,6 kB | Adobe PDF | Open Request a copy |
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