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Mining the Web

Tuesday, November 16, 2010. Using Language Models for Information Retrieval (Python). Continuing my regard for Bayes' Theorem. I decided to write a small python program that will crawl a list of urls and then will allow the user to search that list. I used the query likelihood. Language Model for information retrieval. Chapter. Here is the excerpt from that chapter:. In the query likelihood model. We construct from each document d. In the collection a language model Md. We rank documents by P(d q). 2 Def...

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Tuesday, November 16, 2010. Using Language Models for Information Retrieval (Python). Continuing my regard for Bayes' Theorem. I decided to write a small python program that will crawl a list of urls and then will allow the user to search that list. I used the query likelihood. Language Model for information retrieval. Chapter. Here is the excerpt from that chapter:. In the query likelihood model. We construct from each document d. In the collection a language model Md. We rank documents by P(d q). 2 Def...

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2 1 import langmodel

3 langmodel crawl u

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5 langmodel search 'your query

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Mining the Web | raza-rizvi.blogspot.com Reviews

https://raza-rizvi.blogspot.com

Tuesday, November 16, 2010. Using Language Models for Information Retrieval (Python). Continuing my regard for Bayes' Theorem. I decided to write a small python program that will crawl a list of urls and then will allow the user to search that list. I used the query likelihood. Language Model for information retrieval. Chapter. Here is the excerpt from that chapter:. In the query likelihood model. We construct from each document d. In the collection a language model Md. We rank documents by P(d q). 2 Def...

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Mining the Web: November 2010

http://raza-rizvi.blogspot.com/2010_11_01_archive.html

Tuesday, November 16, 2010. Using Language Models for Information Retrieval (Python). Continuing my regard for Bayes' Theorem. I decided to write a small python program that will crawl a list of urls and then will allow the user to search that list. I used the query likelihood. Language Model for information retrieval. Chapter. Here is the excerpt from that chapter:. In the query likelihood model. We construct from each document d. In the collection a language model Md. We rank documents by P(d q). 2 Def...

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Mining the Web: March 2010

http://raza-rizvi.blogspot.com/2010_03_01_archive.html

Saturday, March 20, 2010. Creating Spam Filter using Naive Bayes Classifier. Few months ago I gave a lecture to CS students about data mining. I decided to show how a spam filter can be built using simple data mining technique called naive bases classifier. It was an interactive lecture and I was surprised by the students' interest in the field and the questions that they asked. Here are the slides:. Posted by Syed Rizvi. Labels: Naive Bayes Classifier. Subscribe to: Posts (Atom). View my complete profile.

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Mining the Web: Duplicate Detection using MD5 and Jaccard Coefficient in C#

http://raza-rizvi.blogspot.com/2009/11/duplicate-detection-using-md5-and.html

Monday, November 23, 2009. Duplicate Detection using MD5 and Jaccard Coefficient in C#. Duplicate documents are significant issue in context of the Web. Detecting near duplicate documents in a large data set, like the web, is a long standing problem. All the major search engines strive to remove duplicate documents from their search results. In this post I will show you how to create a duplicate detection system using n-grams, MD5 algorithm and Jaccard Coefficient using C#. S1 = CreateShingles(page1);.

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Mining the Web: Bayes' theorem : A Love Story

http://raza-rizvi.blogspot.com/2010/11/bayes-theorem-love-story.html

Sunday, November 14, 2010. Bayes' theorem : A Love Story. A few days ago, I saw the video of Hilary Mason. Presenting the history of machine learning. As far as I know, she has covered the significant developments in the field in the most simplistic, understandable and casual way possible. She shed some light on the fundamental math and algorithmic tools employed in the field. Hilary suggested, that after the AI winter. My first DM project was building a Spam Filter using Bayes' Theorem. During one of th...

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Mining the Web: October 2009

http://raza-rizvi.blogspot.com/2009_10_01_archive.html

Thursday, October 15, 2009. Text Summarization using Vector Space Model. The rapid growth of World Wide Web has resulted in information overload. One cannot possibly read every news article, document, paper, etc that is out there on the web. That is why text summarization. Has become essential in this information age. Even the search engines show the summary. Of each of the search results to help users select the most appropriate link. 1 If the character is "? Then mark this as the end of the sentence.

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Mining the Web

Tuesday, November 16, 2010. Using Language Models for Information Retrieval (Python). Continuing my regard for Bayes' Theorem. I decided to write a small python program that will crawl a list of urls and then will allow the user to search that list. I used the query likelihood. Language Model for information retrieval. Chapter. Here is the excerpt from that chapter:. In the query likelihood model. We construct from each document d. In the collection a language model Md. We rank documents by P(d q). 2 Def...

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