Read online ebook Cognitive Technologies: Logical and Relational Learning by Luc De Raedt MOBI, EPUB, FB2
9783540200406 3540200401 Iusethetermlogicalandrelationallearning torefertothesub'eldofarti'cial intelligence, machinelearninganddataminingthatisconcernedwithlearning in expressive logical or relational representations. It is the union of inductive logic programming, (statistical) relational learning and multi-relational data mining, which all have contributed techniques for learning from data in re- tional form. Even though some early contributions to logical and relational learning are about forty years old now, it was only with the advent of - ductive logic programming in the early 1990s that the ?eld became popular. Whereas initial work was often concerned with logical (or logic programming) issues, thefocushasrapidlychangedtothediscoveryofnewandinterpretable knowledge from structured data, often in the form of rules, and soon imp- tant successes in applications in domains such as bio- and chemo-informatics and computational linguistics were realized. Today, the challenges and opp- tunities of dealing with structured data and knowledge have been taken up by the arti'cial intelligence community at large and form the motivation for a lot of ongoing research. Indeed, graph, network and multi-relational data mining are now popular themes in data mining, and statistical relational learning is receiving a lot of attention in the machine learning and uncertainty in art- cial intelligence communities. In addition, the range of tasks for which logical and relational techniques have been developed now covers almost all machine learning and data mining task, This first textbook on multi-relational data mining and inductive logic programming provides a complete overview of the field. It is self-contained and easily accessible for graduate students and practitioners of data mining and machine learning., This is the first textbook on inductive logic programming (ILP) and multi-relational data mining (MRDM). These subfields of data mining and machine learning are concerned with analyzing structured data that arise in numerous applications, such as bioinformatics, Web mining, natural language processing, etc. The author explains some important techniques in detail by using case studies centered around well-known ILP or MRDM systems. These studies are among the "classics" in the field and they also provide a good starting point for a more general discussion. Related systems and techniques are covered in detailed bibliographies in each chapter. The book addresses graduate students in computer science, databases and artificial intelligence, as well as practitioners of data mining and machine learning., This textbook covers logical and relational learning in depth, and hence provides an introduction to inductive logic programming (ILP), multirelational data mining (MRDM) and (statistical) relational learning (SRL). These subfields of data mining and machine learning are concerned with the analysis of complex and structured data sets that arise in numerous applications, such as bio- and chemoinformatics, network analysis, Web mining, natural language processing, within the rich representations offered by relational databases and computational logic.The author introduces the machine learning and representational foundations of the field and explains some important techniques in detail by using some of the classic case studies centered around well-known ILP, MRDM and SRL systems. The book is suitable for use in graduate courses and should be of interest to graduate students and researchers in computer science, databases and artificial intelligence, as well as practitioners of data mining and machine learning. It contains numerous figures and exercises, and slides are available for many chapters.
9783540200406 3540200401 Iusethetermlogicalandrelationallearning torefertothesub'eldofarti'cial intelligence, machinelearninganddataminingthatisconcernedwithlearning in expressive logical or relational representations. It is the union of inductive logic programming, (statistical) relational learning and multi-relational data mining, which all have contributed techniques for learning from data in re- tional form. Even though some early contributions to logical and relational learning are about forty years old now, it was only with the advent of - ductive logic programming in the early 1990s that the ?eld became popular. Whereas initial work was often concerned with logical (or logic programming) issues, thefocushasrapidlychangedtothediscoveryofnewandinterpretable knowledge from structured data, often in the form of rules, and soon imp- tant successes in applications in domains such as bio- and chemo-informatics and computational linguistics were realized. Today, the challenges and opp- tunities of dealing with structured data and knowledge have been taken up by the arti'cial intelligence community at large and form the motivation for a lot of ongoing research. Indeed, graph, network and multi-relational data mining are now popular themes in data mining, and statistical relational learning is receiving a lot of attention in the machine learning and uncertainty in art- cial intelligence communities. In addition, the range of tasks for which logical and relational techniques have been developed now covers almost all machine learning and data mining task, This first textbook on multi-relational data mining and inductive logic programming provides a complete overview of the field. It is self-contained and easily accessible for graduate students and practitioners of data mining and machine learning., This is the first textbook on inductive logic programming (ILP) and multi-relational data mining (MRDM). These subfields of data mining and machine learning are concerned with analyzing structured data that arise in numerous applications, such as bioinformatics, Web mining, natural language processing, etc. The author explains some important techniques in detail by using case studies centered around well-known ILP or MRDM systems. These studies are among the "classics" in the field and they also provide a good starting point for a more general discussion. Related systems and techniques are covered in detailed bibliographies in each chapter. The book addresses graduate students in computer science, databases and artificial intelligence, as well as practitioners of data mining and machine learning., This textbook covers logical and relational learning in depth, and hence provides an introduction to inductive logic programming (ILP), multirelational data mining (MRDM) and (statistical) relational learning (SRL). These subfields of data mining and machine learning are concerned with the analysis of complex and structured data sets that arise in numerous applications, such as bio- and chemoinformatics, network analysis, Web mining, natural language processing, within the rich representations offered by relational databases and computational logic.The author introduces the machine learning and representational foundations of the field and explains some important techniques in detail by using some of the classic case studies centered around well-known ILP, MRDM and SRL systems. The book is suitable for use in graduate courses and should be of interest to graduate students and researchers in computer science, databases and artificial intelligence, as well as practitioners of data mining and machine learning. It contains numerous figures and exercises, and slides are available for many chapters.