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 Framework, Design, and Application Hidden Markov Model for Text Classification using LC Classification.
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Framework, Design, and Application Hidden Markov Model for Text Classification using LC Classification.

автор Kwan Y. издательство Книга по Требованию Локальные и корпоративные сети
Cтраниц 180        мягкая обложка       
Аннотация:Two primary objectives are: The proposed model is compared to a Naive Bayesian model, which has been extensively used in TC applications. Text classification (TC) is the task of automatically categorizing text into pre-set categories by analyzing their contents. First, the development of a new TC model based on hidden Markov model (HMM) proposed as a new framework for TC task. A general prototype for an HMM-based TC model is proposed and implemented, so as to classify a collection of dissertation abstracts from the ProQuest Digital Dissertations database into LCC. HMM has been applied to a wide range of applications in text processing such as text segmentation and event tracking, information retrieval, and information extraction. Lastly, current TC challenges and issues are discussed. Few, however, have applied HMM to TC. Second, the application of the Library of Congress Classification (LCC) as a classification scheme for automatically organizing digital resources. In this monograph, a framework of a new TC model based on hidden Markov model is proposed, and its implementation is demonstrated in an application of library and information science.
Автор: Kwan Y.
Издательство: Книга по Требованию
Год:2008
Цена:
2783 руб
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Поиск: Framework, Design, and Application Hidden Markov Model for Text Classification using LC Classification.
НОВЫЙ ГОД СИМВОЛ 2013 ГОДАУКРАШЕНИЯ
КАРНАВАЛ НА НОВЫЙ ГОД РАСПРОДАЖА
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Evaluating the GPRS Radio Interface for Different Quality of Service...

I co ras o he models developed i , our approach addi io ally represe s he mobili y of users hrough arrival ra es of ew GSM a d GPRS users as well as ha dover ra es of GSM a d GPRS users from eighbori g cells. Fur hermore, we co sider users wi h differe QoS profiles modeled by a weigh ed fair queuei g scheme accordi g o . he remai der of he paper is orga ized as follows. Sec io 2 describes he basic GPRS e work archi ec ure, he radio i erface, a d differe QoS profiles, which will be co sidered i he simula or. I Sec io 3 we describe he sof ware archi ec ure of he GPRS simula or, de ails abou he mobili y of GSM a d GPRS users, he way we modeled quali y of service profiles, a d he workload model we used. Resul s of he simula io s udies are prese ed i Sec io 4. We provide curves for average carried raffic a d packe loss probabili ies for differe cha el alloca io schemes a d packe priori ies as well as curves for average hroughpu per GPRS user.3 he Simula io Model We co sider a clus er comprisi g of sever hexadiago al cells i a i egra ed GSM/GPRS e work, servi g circui -swi ched voice a d packe -swi ched da a calls. he performa ce s udies prese ed i Sec io 4 were co duc ed for he i ermos cell of he seve cell clus er.
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