Search Results - (( text classification parallel algorithm ) OR ( java application max algorithm ))
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Batch mode heuristic approaches for efficient task scheduling in grid computing system
Published 2016“…To address these problems, this research proposes three new distributed static batch mode inspired algorithms. The first (proposed) algorithm is based on Min-Min, called Min-Diff, the second algorithm is based on Max-Min, called Max-Average, and the third algorithm is to handle the load balancing, called Efficient Load Balancing (ELB). …”
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Text classification using Naive Bayes: An experiment to conference paper
Published 2005“…The basic text classification technique in forum application has been discussed in Sainin (2005a) and Sainin (2005b).The paper explains about the use of the basic naïve Bayes algorithm to classify forum text me ssages into two classes namely clean and bad. …”
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Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining.
Published 2005“…The accuracy for rules and classification resulted from the TIP method are compared with other methods such as Standard Integer Programming (SIP) and Decision Related Integer Programming (DRIP) from Rough Set, Genetic Algorithm (GA), Johnson reducer, HoltelR method, Multiple Regression (MR), Neural Network (NN), Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5); all other classifiers that are mostly used in the classification tasks. …”
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Development of a parallel clustering of bilingual corpora based on reduced terms
Published 2015“…However, not many works conducted that are related to clustering bilingual documents found, especially for Malay text articles. The quality of clustering bilingual text documents is highly influenced by the quality of the bag-of-word presentation of Malay text articles presented to the clustering algorithm. …”
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Projecting named entity tags from a resource rich language to a resource poor language
Published 2012“…Named Entity Recognition (NER) is the identification of words in text that correspond to a pre-defined taxonomy such as person, organization, location, date, time, etc.This article focuses on the person (PER), organization (ORG) and location (LOC) entities for a Malay journalistic corpus of terrorism.A projection algorithm, using the Dice Coefficient function and bigram scoring method with domain-specific rules, is suggested to map the NE information from the English corpus to the Malay corpus of terrorism.The English corpus is the translated version of the Malay corpus.Hence, these two corpora are treated as parallel corpora. …”
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Projecting named entity tags from a resource rich language to a resource poor language
Published 2013“…Named Entity Recognition (NER) is the identification of words in text that correspond to a pre-defined taxonomy such as person, organization, location, date, time, etc. …”
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Projecting named entity tags from a resource rich language to a resource poor language
Published 2013“…Named Entity Recognition (NER) is the identification of words in text that correspond to a pre-defined taxonomy such as person, organization, location, date, time, etc. …”
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Projecting named entity tags from a resource rich language to a resource poor language
Published 2013“…Named Entity Recognition (NER) is the identification of words in text that correspond to a pre-defined taxonomy such as person, organization, location, date, time, etc. …”
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Hybrid harmony search-artificial intelligence models in credit scoring
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