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{ "item_title" : "Machine Learning Methods for Commonsense Reasoning Processes", "item_author" : [" Xenia Naidenova "], "item_description" : "The reduction of machine learning algorithms to commonsense reasoning processes is now possible due to the reformulation of machine learning problems as searching the best approximation of a given classification on a given set of examples. Machine Learning Methods for Commonsense Reasoning Processes: Interactive Models provides a unique view on classification as a key to human commonsense reasoning and transforms traditional considerations of data and knowledge communications. Containing leading research evolved from international investigations, this book presents an effective classification of logical rules used in the modeling of commonsense reasoning.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/1/60/566/810/1605668109_b.jpg", "price_data" : { "retail_price" : "180.00", "online_price" : "180.00", "our_price" : "180.00", "club_price" : "180.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Machine Learning Methods for Commonsense Reasoning Processes|Xenia Naidenova

Machine Learning Methods for Commonsense Reasoning Processes : Interactive Models

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Overview

The reduction of machine learning algorithms to commonsense reasoning processes is now possible due to the reformulation of machine learning problems as searching the best approximation of a given classification on a given set of examples. Machine Learning Methods for Commonsense Reasoning Processes: Interactive Models provides a unique view on classification as a key to human commonsense reasoning and transforms traditional considerations of data and knowledge communications. Containing leading research evolved from international investigations, this book presents an effective classification of logical rules used in the modeling of commonsense reasoning.

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Details

  • ISBN-13: 9781605668109
  • ISBN-10: 1605668109
  • Publisher: Information Science Reference
  • Publish Date: October 2009
  • Dimensions: 11.2 x 8.6 x 1.2 inches
  • Shipping Weight: 3.3 pounds
  • Page Count: 426

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