Machine learning methods for commonsense reasoning processes - Info and Reading Options
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By Xenia Naidenova
"Machine learning methods for commonsense reasoning processes" was published by Information Science Reference in 2010 - Hershey, PA, it has 410 pages and the language of the book is English.
“Machine learning methods for commonsense reasoning processes” Metadata:
- Title: ➤ Machine learning methods for commonsense reasoning processes
- Author: Xenia Naidenova
- Language: English
- Number of Pages: 410
- Publisher: Information Science Reference
- Publish Date: 2010
- Publish Location: Hershey, PA
“Machine learning methods for commonsense reasoning processes” Subjects and Themes:
- Subjects: Recursive partitioning - Correlation (Statistics) - Machine learning - Regression analysis
Edition Specifications:
- Pagination: xiv, 410 p. :
Edition Identifiers:
- The Open Library ID: OL24460635M - OL15498552W
- Library of Congress Control Number (LCCN): 2009021595
- ISBN-13: 9781605668109 - 9781605668116
- All ISBNs: 9781605668109 - 9781605668116
AI-generated Review of “Machine learning methods for commonsense reasoning processes”:
"Machine learning methods for commonsense reasoning processes" Table Of Contents:
- 1- Knowledge in the psychology of thinking and mathematics
- 2- Logic-based reasoning in the framework of artiticial intelligence
- 3- The coordination of commonsense reasoning operations
- 4- The logical rules of commonsense reasoning
- 5- The examples of human connonsense reasoning processes
- 6- Machine learning (ML) as a diagnostic task
- 7- The concept of good classification (diagnostic) test
- 8- The duality of good diagnostic tests
- 9- Towards an integrative model of deductive-inductive commonsense reasoning
- 10- Towards a model of fuzzy commonsense reasoning
- 11- Object-oriented technology for expert system generation
- 12- Case technology for psycho-diagnostic system generation
- 13- Commonsense reasoning in intelligent computer systems.
"Machine learning methods for commonsense reasoning processes" Description:
The Open Library:
"The main purpose of this book is to demonstrate the possibility of transforming a large class of machine learning algorithms into integrated commonsense reasoning processes in which inductive and deductive inferences are not separated one from another but moreover they are correlated and support one another"--Provided by publisher.
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