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{ "item_title" : "Fusion Methods for Time-Series Classification", "item_author" : [" Martin Schader", "Krisztian Buza "], "item_description" : "Time-series classification is the common theoretical background of many recognition tasks performed by computers, such as handwriting recognition, speech recognition or detection of abnormalities in electrocardiograph signals. In this book, the state-of-the-art in time-series classification is surveyed and five new techniques are presented. Four out of them aim at making the recognition more accurate, while the proposed instance-selection algorithm speeds up time-series classification. Besides time-series classification tasks, potential applications of the proposed techniques include problems from various domains, e.g. web science or medicine.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/3/63/163/085/3631630859_b.jpg", "price_data" : { "retail_price" : "59.20", "online_price" : "59.20", "our_price" : "59.20", "club_price" : "59.20", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Fusion Methods for Time-Series Classification|Martin Schader

Fusion Methods for Time-Series Classification

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Overview

Time-series classification is the common theoretical background of many recognition tasks performed by computers, such as handwriting recognition, speech recognition or detection of abnormalities in electrocardiograph signals. In this book, the state-of-the-art in time-series classification is surveyed and five new techniques are presented. Four out of them aim at making the recognition more accurate, while the proposed instance-selection algorithm speeds up time-series classification. Besides time-series classification tasks, potential applications of the proposed techniques include problems from various domains, e.g. web science or medicine.

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Details

  • ISBN-13: 9783631630853
  • ISBN-10: 3631630859
  • Publisher: Peter Lang Gmbh, Internationaler Verlag Der W
  • Publish Date: November 2011
  • Page Count: 144

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