أعرض تسجيلة المادة بشكل مبسط

dc.creator Castillo Sobrino, María Dolores del
dc.creator Serrano Moreno, José Ignacio
dc.date 2007-11-20T09:11:03Z
dc.date 2007-11-20T09:11:03Z
dc.date 2005
dc.date.accessioned 2017-01-31T00:58:49Z
dc.date.available 2017-01-31T00:58:49Z
dc.identifier Mathware and Soft Computing, 2005, 12 (1): 15-32.
dc.identifier 1134-5632
dc.identifier http://hdl.handle.net/10261/2224
dc.identifier.uri http://dspace.mediu.edu.my:8181/xmlui/handle/10261/2224
dc.description The goal of the research described here is to develop a multistrategy classifier system that can be used for document categorization. The system automatically discovers classification patterns by applying several empirical learning methods to different representations for preclassified documents. The learners work in a parallel manner, where each learner carries out its own feature selection based on evolutionary techniques and then obtains a classification model. In classifying documents, the system combines the predictions of the learners by applying evolutionary techniques as well. The system relies on a modular , flexible architecture that makes no assumptions about the design of learners or the number of learners available and guarantees the independence of the thematic domain.
dc.description Part of this work was supported by the Spanish Ministry of Science and Technology under project FIT-070000-2001-193 and by Optenet, S.A.
dc.description Peer reviewed
dc.language eng
dc.publisher Universidad Politécnica de Cataluña
dc.rights openAccess
dc.subject Categorization
dc.subject Classification models
dc.subject Multivariant analysis
dc.subject Artificial Intelligence
dc.title A multistrategy approach for digital text categorization
dc.title Un enfoque multiestratégico para la categorización de textos digitales
dc.type Artículo


الملفات في هذه المادة

الملفات الحجم الصيغة عرض

لا توجد أي ملفات مرتبطة بهذه المادة.

هذه المادة تبدو في المجموعات التالية:

أعرض تسجيلة المادة بشكل مبسط