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{ "item_title" : "Image Classification Using Python and Techniques of Computer Vision and Machine Learning", "item_author" : [" John Magic", "Mark Magic "], "item_description" : "This book implemented six different algorithms to classify images with prediction accuracy as the primary criterion and time consumption as the secondary one. The accuracies varied between about 30% and 90%, while the time consumptions varied from several seconds to more than one hour. Considering both criteria, the Pre-Trained AlexNet Features Representation plus a Classifier, such as the k-Nearest Neighbors (KNN) and the Support Vector Machines (SVM), was concluded as the best algorithm.", "item_img_path" : "https://covers1.booksamillion.com/covers/bam/1/79/660/726/1796607266_b.jpg", "price_data" : { "retail_price" : "44.99", "online_price" : "44.99", "our_price" : "44.99", "club_price" : "44.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Image Classification Using Python and Techniques of Computer Vision and Machine Learning|John Magic

Image Classification Using Python and Techniques of Computer Vision and Machine Learning

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Overview

This book implemented six different algorithms to classify images with prediction accuracy as the primary criterion and time consumption as the secondary one. The accuracies varied between about 30% and 90%, while the time consumptions varied from several seconds to more than one hour. Considering both criteria, the Pre-Trained AlexNet Features Representation plus a Classifier, such as the k-Nearest Neighbors (KNN) and the Support Vector Machines (SVM), was concluded as the best algorithm.

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Details

  • ISBN-13: 9781796607260
  • ISBN-10: 1796607266
  • Publisher: Independently Published
  • Publish Date: January 2019
  • Dimensions: 9 x 6 x 0.24 inches
  • Shipping Weight: 0.36 pounds
  • Page Count: 116

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