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{ "item_title" : "Challenges in Machine Generation of Analytic Products from Multi-Source Data", "item_author" : [" National Academies of Sciences Engineeri", "Division on Engineering and Physical Sci", "Intelligence Community Studies Board "], "item_description" : "The Intelligence Community Studies Board of the National Academies of Sciences, Engineering, and Medicine convened a workshop on August 9-10, 2017 to examine challenges in machine generation of analytic products from multi-source data. Workshop speakers and participants discussed research challenges related to machine-based methods for generating analytic products and for automating the evaluation of these products, with special attention to learning from small data, using multi-source data, adversarial learning, and understanding the human-machine relationship. This publication summarizes the presentations and discussions from the workshop.", "item_img_path" : "https://covers4.booksamillion.com/covers/bam/0/30/946/573/0309465737_b.jpg", "price_data" : { "retail_price" : "55.00", "online_price" : "55.00", "our_price" : "55.00", "club_price" : "55.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Challenges in Machine Generation of Analytic Products from Multi-Source Data|National Academies of Sciences Engineeri

Challenges in Machine Generation of Analytic Products from Multi-Source Data : Proceedings of a Workshop

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Overview

The Intelligence Community Studies Board of the National Academies of Sciences, Engineering, and Medicine convened a workshop on August 9-10, 2017 to examine challenges in machine generation of analytic products from multi-source data. Workshop speakers and participants discussed research challenges related to machine-based methods for generating analytic products and for automating the evaluation of these products, with special attention to learning from small data, using multi-source data, adversarial learning, and understanding the human-machine relationship. This publication summarizes the presentations and discussions from the workshop.

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Details

  • ISBN-13: 9780309465731
  • ISBN-10: 0309465737
  • Publisher: National Academies Press
  • Publish Date: December 2017
  • Dimensions: 10.8 x 8.36 x 0.25 inches
  • Shipping Weight: 0.57 pounds
  • Page Count: 70

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