What is knowledge and how is it represented? This book focuses on
the idea of formalising knowledge as relations, interpreting
knowledge represented in databases or logic programs as relational
data and discovering new knowledge by identifying hidden and
defining new relations. After a brief introduction to
representational issues, the author develops a relational language
for abstract machine learning problems. He then uses this language
to discuss traditional methods such as clustering and decision tree
induction, before moving onto two previously underestimated topics
that are just coming to the fore: rough set data analysis and
inductive logic programming. Its clear and precise presentation is
ideal for undergraduate computer science students. The book will
also interest those who study artificial intelligence or machine
learning at the graduate level. Exercises are provided and each
concept is introduced using the same example domain, making it
easier to compare the individual properties of different
approaches.
General
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