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| 001 | 233328 | ||
| 003 | koha_MIRAKIL | ||
| 005 | 20221226090135.0 | ||
| 008 | 190118b tu 000 0 | ||
| 040 |
_aCY-NiCIU _btur _cCY-NiCIU _erda |
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| 041 | 0 | _aeng | |
| 090 |
_aYL 391 _b A35 2014 |
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| 100 | 1 | _aAdi, Abdulwahab O. | |
| 245 | 0 |
_aDocument classification using naive bayes algorithm _cAbdulwahab O. Adi; Supervisor: Erbuğ Çelebi |
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| 260 |
_aNicosia _bCyprus International University _c2014 |
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| 300 |
_aIX, 49 p. _bfigure _c30.5 cm _eCD |
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| 336 |
_2rdacontent _atext _btxt |
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| 337 |
_2rdamedia _aunmediated _bn |
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| 338 |
_2rdacarrier _avolume _bnc |
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| 500 | _3Includes CD | ||
| 520 | _a'ABSTRACT In this study, we have implemented a naïve Bayes Classifier in the Java Language. The classifier was tested on the popular 20 News group data set for majority of document categorization and clustering algorithm implementation. The ultimate object is for better understanding of the algorithm as an a way for automatic document categorization is done and also to be able to ponder new methods that can be proposed for future research purposes. At the end of this research, we successfully tested the performance of our implementation using three methods. The accuracy was measured by comparing it's with the accuracies of other algorithms using the same dataset. It turned out to work as postulated theoretically in normal academic environs. Also, we were able to conclude that the naïve Bayes classifier performs well among other similar classifiers but it also has its short comings as well. Keywords: Bayes Theorem, Supervised Learning, Document Classification, Naïve Bayes Classifier, Tokenization, Stemming, Machine Learning, Information Retrieval, Java ' | ||
| 650 | 0 | 0 | _aMakine öğrenme |
| 650 | 0 | 0 | _aMachine learning |
| 650 | 0 | 0 | _aBayes teoremi |
| 650 | 0 | 0 | _aBayes Theorem |
| 700 | 0 |
_aSupervisor: Çelebi, Erbuğ _91656 |
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| 942 |
_2ddc _cTS |
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| 505 | 1 |
_g1 _tCHAPTER ONE |
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| 505 | 1 |
_g1 _tINTRODUCTION |
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| 505 | 1 |
_g3 _tObjectives |
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| 505 | 1 |
_g3 _tOrganization of Thesis |
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| 505 | 1 |
_g4 _tCHAPTER 2 |
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| 505 | 1 |
_g4 _tLITERATURE REVIEW |
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| 505 | 1 |
_g4 _tMachine Learning |
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| 505 | 1 |
_g5 _tSupervised Learning |
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| 505 | 1 |
_g7 _tUnsupervised Learning |
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| 505 | 1 |
_g7 _tSemi-Supervised Learning |
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| 505 | 1 |
_g8 _tReinforcement Learning |
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| 505 | 1 |
_g8 _tTransduction |
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| 505 | 1 |
_g10 _tLearning to Learn |
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| 505 | 1 |
_g10 _tDevelopmental Learning |
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| 505 | 1 |
_g11 _tPREVIOUS WORK DONE |
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| 505 | 1 |
_g11 _tNaive Bayes Classifier As A Spam Detector |
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| 505 | 1 |
_g12 _tNaive Bayes Classifier in Sentiment Analysis |
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| 505 | 1 |
_g13 _tNaive Bayes Classifier in Cancer Diagnosis |
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| 505 | 1 |
_g14 _tNaive Bayes Classifier in Plant Specie classification |
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| 505 | 1 |
_g15 _tCHAPTER 3 |
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| 505 | 1 |
_g15 _tNAİVE BAYES CLASSIFIER |
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| 505 | 1 |
_g15 _tBayes Theorem |
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| 505 | 1 |
_g15 _tText Classification Simplified |
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| 505 | 1 |
_g18 _tPrior Probability,P(c) |
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| 505 | 1 |
_g19 _tLikelihood Probability, Pd/c |
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| 505 | 1 |
_g20 _tLaplace Smoothening |
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| 505 | 1 |
_g22 _tSimple Text Classification Examples |
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| 505 | 1 |
_g26 _tCHAPTER 4 |
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| 505 | 1 |
_g26 _tIMPLEMENTATION |
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| 505 | 1 |
_g26 _tIntroduction |
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| 505 | 1 |
_g26 _tJava an NLP Libraries |
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| 505 | 1 |
_g27 _tProgram Design |
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| 505 | 1 |
_g27 _tExperimental Setup |
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| 505 | 1 |
_g28 _tLoading the data set |
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| 505 | 1 |
_g29 _tStop Word Removal |
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| 505 | 1 |
_g30 _tTokenization |
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| 505 | 1 |
_g33 _tStemming |
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| 505 | 1 |
_g36 _tBag of Word Creation |
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| 505 | 1 |
_g39 _tEvaluation |
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| 505 | 1 |
_g39 _tClassification |
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| 505 | 1 |
_g40 _tDesign Summary |
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| 505 | 1 |
_g41 _tCHAPTER 5 |
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| 505 | 1 |
_g41 _tEVALUATION |
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| 505 | 1 |
_g41 _tCross Validation method |
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| 505 | 1 |
_g41 _tComparison with other Classifier Application |
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| 505 | 1 |
_g42 _tIcsiboost-bigram |
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| 505 | 1 |
_g42 _tExpected Maximum alorithm |
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| 505 | 1 |
_g42 _tVaried Training Set based Evaluation |
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| 505 | 1 |
_g43 _tRESULTS OF EVALUATION PROCEDURES |
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| 505 | 1 |
_g43 _tCross Validation Method |
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| 505 | 1 |
_g44 _tComparison with other Classifier Programs |
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| 505 | 1 |
_g45 _tVaried Training Set based Evaluation |
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| 505 | 1 |
_g46 _tCHAPTER 6 |
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| 505 | 1 |
_g46 _tCONCLUSION AND FUTURE WORK |
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| 505 | 1 |
_g47 _tREFERENCES |
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| 999 |
_c434 _d434 |
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