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{ "item_title" : "Nature-Inspired Computation in Data Mining and Machine Learning", "item_author" : [" Xin-She Yang", "Xing-Shi He "], "item_description" : "Adaptive Improved Flower Pollination Algorithm for Global Optimization.- Algorithms for Optimization and Machine Learning over Cloud.- Implementation of Machine Learning and Data Mining to Improve Cybersecurity and Limit Vulnerabilities to Cyber Attacks.- Comparative analysis of different classifiers on crisis-related tweets: An elaborate study.- An Improved Extreme Learning Machine Tuning by Flower Pollination Algorithm.- Prospects of Machine and Deep Learning in Analysis of Vital Signs for the Improvement of Healthcare Services.", "item_img_path" : "https://covers4.booksamillion.com/covers/bam/3/03/028/555/3030285553_b.jpg", "price_data" : { "retail_price" : "119.99", "online_price" : "119.99", "our_price" : "119.99", "club_price" : "119.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Nature-Inspired Computation in Data Mining and Machine Learning|Xin-She Yang

Nature-Inspired Computation in Data Mining and Machine Learning

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Overview

Adaptive Improved Flower Pollination Algorithm for Global Optimization.- Algorithms for Optimization and Machine Learning over Cloud.- Implementation of Machine Learning and Data Mining to Improve Cybersecurity and Limit Vulnerabilities to Cyber Attacks.- Comparative analysis of different classifiers on crisis-related tweets: An elaborate study.- An Improved Extreme Learning Machine Tuning by Flower Pollination Algorithm.- Prospects of Machine and Deep Learning in Analysis of Vital Signs for the Improvement of Healthcare Services.

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Details

  • ISBN-13: 9783030285555
  • ISBN-10: 3030285553
  • Publisher: Springer
  • Publish Date: September 2020
  • Dimensions: 9.21 x 6.14 x 0.6 inches
  • Shipping Weight: 0.9 pounds
  • Page Count: 273

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