Synopses & Reviews
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. This book presents an effective classification of logical rules used in the modeling of commonsense reasoning.
Synopsis
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.
Synopsis
"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.
Table of Contents
Knowledge in the psychology of thinking and mathematics -- Logic-based reasoning in the framework of artiticial intelligence -- The coordination of commonsense reasoning operations -- The logical rules of commonsense reasoning -- The examples of human connonsense reasoning processes -- Machine learning (ML) as a diagnostic task -- The concept of good classification (diagnostic) test -- The duality of good diagnostic tests -- Towards an integrative model of deductive-inductive commonsense reasoning -- Towards a model of fuzzy commonsense reasoning -- Object-oriented technology for expert system generation -- Case technology for psycho-diagnostic system generation -- Commonsense reasoning in intelligent computer systems.