Data Compression – Exploring New Techniques: Data Compression - An Intelligent Approach

 
9783659135767: Data Compression – Exploring New Techniques: Data Compression - An Intelligent Approach

Our project comprises of analysis of the existing algorithms for data compression so that we can utilize some features of these algorithms use them to build a more efficient algorithm. We have also considered detailed study to observe the efficiency of the algorithms for different document types with the intention of building a comprehensive data compression technique. We need to study and analyze all the algorithms separately to incorporate the positives from each of them and try to rectify the flaws that exist in these algorithms. For clear elucidation of the data compression concepts we try to discuss each standard algorithm in detail. There is a close connection between machine learning and compression: a system that predicts the posterior probabilities of a sequence given its entire history can be used for optimal data compression (by using arithmetic coding on the output distribution), while an optimal compressor can be used for prediction (by finding the symbol that compresses best, given the previous history). This equivalence has been used as justification for data compression as a benchmark for "general intelligence".

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About the Author:

Mr. Debashis Chakraborty is an Assistant Professor in the department of Computer Science and Engineering, st. Thomas’ College of Engineering and Technology, Kolkata, West Bengal, India. He has authored or co-authored over 10 conference and journal papers in area of Data and Image Compression.

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Book Description Book Condition: New. Publisher/Verlag: LAP Lambert Academic Publishing | Data Compression - An Intelligent Approach | Our project comprises of analysis of the existing algorithms for data compression so that we can utilize some features of these algorithms use them to build a more efficient algorithm. We have also considered detailed study to observe the efficiency of the algorithms for different document types with the intention of building a comprehensive data compression technique. We need to study and analyze all the algorithms separately to incorporate the positives from each of them and try to rectify the flaws that exist in these algorithms. For clear elucidation of the data compression concepts we try to discuss each standard algorithm in detail. There is a close connection between machine learning and compression: a system that predicts the posterior probabilities of a sequence given its entire history can be used for optimal data compression (by using arithmetic coding on the output distribution), while an optimal compressor can be used for prediction (by finding the symbol that compresses best, given the previous history). This equivalence has been used as justification for data compression as a benchmark for "general intelligence". | Format: Paperback | Language/Sprache: english | 72 pp. Bookseller Inventory # K9783659135767

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Book Description LAP Lambert Academic Publishing Mai 2012, 2012. Taschenbuch. Book Condition: Neu. Neuware - Our project comprises of analysis of the existing algorithms for data compression so that we can utilize some features of these algorithms use them to build a more efficient algorithm. We have also considered detailed study to observe the efficiency of the algorithms for different document types with the intention of building a comprehensive data compression technique. We need to study and analyze all the algorithms separately to incorporate the positives from each of them and try to rectify the flaws that exist in these algorithms. For clear elucidation of the data compression concepts we try to discuss each standard algorithm in detail. There is a close connection between machine learning and compression: a system that predicts the posterior probabilities of a sequence given its entire history can be used for optimal data compression (by using arithmetic coding on the output distribution), while an optimal compressor can be used for prediction (by finding the symbol that compresses best, given the previous history). This equivalence has been used as justification for data compression as a benchmark for 'general intelligence'. 72 pp. Englisch. Bookseller Inventory # 9783659135767

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Book Description LAP Lambert Academic Publishing Mai 2012, 2012. Taschenbuch. Book Condition: Neu. Neuware - Our project comprises of analysis of the existing algorithms for data compression so that we can utilize some features of these algorithms use them to build a more efficient algorithm. We have also considered detailed study to observe the efficiency of the algorithms for different document types with the intention of building a comprehensive data compression technique. We need to study and analyze all the algorithms separately to incorporate the positives from each of them and try to rectify the flaws that exist in these algorithms. For clear elucidation of the data compression concepts we try to discuss each standard algorithm in detail. There is a close connection between machine learning and compression: a system that predicts the posterior probabilities of a sequence given its entire history can be used for optimal data compression (by using arithmetic coding on the output distribution), while an optimal compressor can be used for prediction (by finding the symbol that compresses best, given the previous history). This equivalence has been used as justification for data compression as a benchmark for 'general intelligence'. 72 pp. Englisch. Bookseller Inventory # 9783659135767

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Book Description LAP Lambert Academic Publishing, Germany, 2012. Paperback. Book Condition: New. Aufl.. Language: English . Brand New Book ***** Print on Demand *****.Our project comprises of analysis of the existing algorithms for data compression so that we can utilize some features of these algorithms use them to build a more efficient algorithm. We have also considered detailed study to observe the efficiency of the algorithms for different document types with the intention of building a comprehensive data compression technique. We need to study and analyze all the algorithms separately to incorporate the positives from each of them and try to rectify the flaws that exist in these algorithms. For clear elucidation of the data compression concepts we try to discuss each standard algorithm in detail. There is a close connection between machine learning and compression: a system that predicts the posterior probabilities of a sequence given its entire history can be used for optimal data compression (by using arithmetic coding on the output distribution), while an optimal compressor can be used for prediction (by finding the symbol that compresses best, given the previous history). This equivalence has been used as justification for data compression as a benchmark for general intelligence. Bookseller Inventory # AAV9783659135767

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Book Description LAP Lambert Academic Publishing Mai 2012, 2012. Taschenbuch. Book Condition: Neu. This item is printed on demand - Print on Demand Neuware - Our project comprises of analysis of the existing algorithms for data compression so that we can utilize some features of these algorithms use them to build a more efficient algorithm. We have also considered detailed study to observe the efficiency of the algorithms for different document types with the intention of building a comprehensive data compression technique. We need to study and analyze all the algorithms separately to incorporate the positives from each of them and try to rectify the flaws that exist in these algorithms. For clear elucidation of the data compression concepts we try to discuss each standard algorithm in detail. There is a close connection between machine learning and compression: a system that predicts the posterior probabilities of a sequence given its entire history can be used for optimal data compression (by using arithmetic coding on the output distribution), while an optimal compressor can be used for prediction (by finding the symbol that compresses best, given the previous history). This equivalence has been used as justification for data compression as a benchmark for 'general intelligence'. 72 pp. Englisch. Bookseller Inventory # 9783659135767

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