Log in
Language:

MERAL Myanmar Education Research and Learning Portal

  • Top
  • Universities
  • Ranking
To
lat lon distance
To

Field does not validate



Index Link

Index Tree

Please input email address.

WEKO

One fine body…

WEKO

One fine body…

Item

{"_buckets": {"deposit": "a6327686-e581-4282-b72b-411069550c45"}, "_deposit": {"id": "3962", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "3962"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/3962", "sets": ["1597824273898", "user-ucsy"]}, "communities": ["ucsy"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Implementation of Web Content Mining by Using Bayesian Classifier", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "The web is a huge repository of information and there is a need for categorizing web documents to facilitate the search and retrieval of pages. Existing algorithms rely solely on the text content of the web pages for classification. In text and web page classification, Bayesian prior probabilities are usually based on term frequencies, term counts within a page. This paper presented a Naïve Bayes web page classification system to classify news genres .The features of web news genres are represented as vector representations using TF*IDF functions. For classification, there are two step; first is extracting the features from the web page and second is based on the training set by using Bayes Theorem to determine the categories of unknown web pages such as arts, health and so on. The system usedthese technique minimize the set of resulting pages tothe user when searching and show the users whatinformation is available"}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "news genre classification"}, {"interim": "web content mining"}, {"interim": "Bayes Theorem"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value": []}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_issue": "", "subitem_journal_title": "Fourth Local Conference on Parallel and Soft Computing", "subitem_pages": "", "subitem_volume": ""}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "", "subitem_c_date": "", "subitem_conference_title": "", "subitem_part": "", "subitem_place": "", "subitem_session": "", "subitem_website": ""}]}, "item_1583103211336": {"attribute_name": "Books/reports/chapters", "attribute_value_mlt": [{"subitem_book_title": "", "subitem_isbn": "", "subitem_pages": "", "subitem_place": "", "subitem_publisher": ""}]}, "item_1583103233624": {"attribute_name": "Thesis/dissertations", "attribute_value_mlt": [{"subitem_awarding_university": "", "subitem_supervisor(s)": [{"subitem_supervisor": ""}]}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Yu, Hnin Myat"}, {"subitem_authors_fullname": "Thein, Naychi Lai Lai"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Article"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2009-12-30"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "http://onlineresource.ucsy.edu.mm/handle/123456789/1678"}, "item_title": "Implementation of Web Content Mining by Using Bayesian Classifier", "item_type_id": "21", "owner": "1", "path": ["1597824273898"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000003962", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-08-03"}, "publish_date": "2019-08-03", "publish_status": "0", "recid": "3962", "relation": {}, "relation_version_is_last": true, "title": ["Implementation of Web Content Mining by Using Bayesian Classifier"], "weko_shared_id": -1}
  1. University of Computer Studies, Yangon
  2. Conferences

Implementation of Web Content Mining by Using Bayesian Classifier

http://hdl.handle.net/20.500.12678/0000003962
http://hdl.handle.net/20.500.12678/0000003962
1dca6fb7-5300-4b05-ba08-97fc2ab371dc
a6327686-e581-4282-b72b-411069550c45
Publication type
Article
Upload type
Publication
Title
Title Implementation of Web Content Mining by Using Bayesian Classifier
Language en
Publication date 2009-12-30
Authors
Yu, Hnin Myat
Thein, Naychi Lai Lai
Description
The web is a huge repository of information and there is a need for categorizing web documents to facilitate the search and retrieval of pages. Existing algorithms rely solely on the text content of the web pages for classification. In text and web page classification, Bayesian prior probabilities are usually based on term frequencies, term counts within a page. This paper presented a Naïve Bayes web page classification system to classify news genres .The features of web news genres are represented as vector representations using TF*IDF functions. For classification, there are two step; first is extracting the features from the web page and second is based on the training set by using Bayes Theorem to determine the categories of unknown web pages such as arts, health and so on. The system usedthese technique minimize the set of resulting pages tothe user when searching and show the users whatinformation is available
Keywords
news genre classification, web content mining, Bayes Theorem
Identifier http://onlineresource.ucsy.edu.mm/handle/123456789/1678
Journal articles
Fourth Local Conference on Parallel and Soft Computing
Conference papers
Books/reports/chapters
Thesis/dissertations
Back
0
0
views
downloads
See details
Views Downloads

Versions

Ver.1 2020-09-01 13:49:06.698909
Show All versions

Share

Mendeley Twitter Facebook Print Addthis

Export

OAI-PMH
  • OAI-PMH DublinCore
Other Formats
  • JSON

Confirm


Back to MERAL


Back to MERAL