Automatic Amharic news text classification with C# and Python

 
9783659411809: Automatic Amharic news text classification with C# and Python
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Machine learning approach for text classification is widely used and have shown a promising accuracy. This approach is also applied for Amharic text and have shown an acceptable result. my objective is to apply machine learning approach using naive Bayes to the Amharic news text classification problem, and demonstrate how the resulting model from this approach can be used in a real world environment.300 files with 45,195 words were used; divided into 100 per class category (Politics, Social and Economy/Business). The files were preprocessed and features were extracted out of them for training and testing the naive Bayes classifier.Finally the designed model is implemented using C# and Python.

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Mr.Fasil Alemeye has more than 6 years of experience in deploying and administering, as well as troubleshooting local area networks (LAN) and Wide area networks (WAN). The Author has also developed several software products for various companies in Ethiopia.

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Book Description Condition: New. Publisher/Verlag: LAP Lambert Academic Publishing | Machine learning approach for text classification is widely used and have shown a promising accuracy. This approach is also applied for Amharic text and have shown an acceptable result. my objective is to apply machine learning approach using naive Bayes to the Amharic news text classification problem, and demonstrate how the resulting model from this approach can be used in a real world environment.300 files with 45,195 words were used; divided into 100 per class category (Politics, Social and Economy/Business). The files were preprocessed and features were extracted out of them for training and testing the naive Bayes classifier.Finally the designed model is implemented using C sharp and Python. | Format: Paperback | Language/Sprache: english | 60 pp. Seller Inventory # K9783659411809

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Book Description LAP Lambert Academic Publishing Jun 2013, 2013. Taschenbuch. Condition: Neu. Neuware - Machine learning approach for text classification is widely used and have shown a promising accuracy. This approach is also applied for Amharic text and have shown an acceptable result. my objective is to apply machine learning approach using naive Bayes to the Amharic news text classification problem, and demonstrate how the resulting model from this approach can be used in a real world environment.300 files with 45,195 words were used; divided into 100 per class category (Politics, Social and Economy/Business). The files were preprocessed and features were extracted out of them for training and testing the naive Bayes classifier.Finally the designed model is implemented using C sharp and Python. 60 pp. Englisch. Seller Inventory # 9783659411809

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Book Description LAP Lambert Academic Publishing Jun 2013, 2013. Taschenbuch. Condition: Neu. Neuware - Machine learning approach for text classification is widely used and have shown a promising accuracy. This approach is also applied for Amharic text and have shown an acceptable result. my objective is to apply machine learning approach using naive Bayes to the Amharic news text classification problem, and demonstrate how the resulting model from this approach can be used in a real world environment.300 files with 45,195 words were used; divided into 100 per class category (Politics, Social and Economy/Business). The files were preprocessed and features were extracted out of them for training and testing the naive Bayes classifier.Finally the designed model is implemented using C sharp and Python. 60 pp. Englisch. Seller Inventory # 9783659411809

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Book Description LAP LAMBERT Academic Publishing. Paperback. Condition: New. 60 pages. Dimensions: 8.7in. x 5.9in. x 0.1in.Machine learning approach for text classification is widely used and have shown a promising accuracy. This approach is also applied for Amharic text and have shown an acceptable result. my objective is to apply machine learning approach using naive Bayes to the Amharic news text classification problem, and demonstrate how the resulting model from this approach can be used in a real world environment. 300 files with 45, 195 words were used; divided into 100 per class category (Politics, Social and EconomyBusiness). The files were preprocessed and features were extracted out of them for training and testing the naive Bayes classifier. Finally the designed model is implemented using C and Python. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Paperback. Seller Inventory # 9783659411809

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Book Description LAP Lambert Academic Publishing, 2013. Paperback. Condition: New. Language: English . Brand New Book. Machine learning approach for text classification is widely used and have shown a promising accuracy. This approach is also applied for Amharic text and have shown an acceptable result. my objective is to apply machine learning approach using naive Bayes to the Amharic news text classification problem, and demonstrate how the resulting model from this approach can be used in a real world environment.300 files with 45,195 words were used; divided into 100 per class category (Politics, Social and Economy/Business). The files were preprocessed and features were extracted out of them for training and testing the naive Bayes classifier.Finally the designed model is implemented using C# and Python. Seller Inventory # KNV9783659411809

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Book Description LAP Lambert Academic Publishing Jun 2013, 2013. Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Neuware - Machine learning approach for text classification is widely used and have shown a promising accuracy. This approach is also applied for Amharic text and have shown an acceptable result. my objective is to apply machine learning approach using naive Bayes to the Amharic news text classification problem, and demonstrate how the resulting model from this approach can be used in a real world environment.300 files with 45,195 words were used; divided into 100 per class category (Politics, Social and Economy/Business). The files were preprocessed and features were extracted out of them for training and testing the naive Bayes classifier.Finally the designed model is implemented using C sharp and Python. 60 pp. Englisch. Seller Inventory # 9783659411809

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