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An Inductive Logic Programming Approach to Statistical Relational Learning

investigates the approach of Learning from proofs and the issue of upgrading Fisher Kernels to Relational Fisher Kernels.;

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Inductive Logic Programming

areas of learning in logic, relational learning, relational data mining, statistical relational learning, multi-relational data mining;

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Inductive Logic Programming

logic, ILP provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data.;

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Inductive Logic Programming

provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data.;

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Inductive Logic Programming

; logical foundations; statistical relational learning; probabilistic ILP; implementation and scalability; applications in robotics, cyber security;

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Inductive Logic Programming

submissions. The papers focus on topics such as the inducing of logic programs, learning from data represented with logic, multi-relational machine;

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Inductive Logic Programming

ofinductive reasoning from pattern recognition, through initial approaches to symbolic machinelearning, to recent techniques for learning relational;

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Logical and Relational Learning

This first textbook on multi-relational data mining and inductive logic programming provides a complete overview of the field. It is self;

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Logical and Relational Learning

This first textbook on multi-relational data mining and inductive logic programming provides a complete overview of the field. It is self;

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Markov Logic

Most subfields of computer science have an interface layer via which applications communicate with the infrastructure, and this is key to;

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Inductive Logic Programming

This book constitutes the thoroughly refereed post-proceedings of the 22nd International Conference on Inductive Logic Programming, ILP;

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Markov Logic

Most subfields of computer science have an interface layer via which applications communicate with the infrastructure, and this is key to;

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Inductive Logic Programming

selected for inclusion in the book. All relevant aspects of inductive logic programming are covered ranging from theory to implementations and;

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Relational Knowledge Discovery

brief introduction to representational issues, the author develops a relational language for abstract machine learning problems. He then uses;

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Relational Knowledge Discovery

brief introduction to representational issues, the author develops a relational language for abstract machine learning problems. He then uses;

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Logical Modeling of Biological Systems

of thinking about biology with a dramatic impact on the way that research is performed. The logical approach provides an intuitive method to provide;

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Boosted Statistical Relational Learners

extraction in text to medical problems. Including both context and well-tested applications, Boosting Statistical Relational Learning from;

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C++ Programs to Accompany Programming Logic and Design

. Together, the two books provide the perfect opportunity for readers to learn the fundamentals of programming, while also learning an actual leading;

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Java Programs for Programming Logic and Design

. Together, the two books provide the perfect opportunity for readers to learn the fundamentals of programming, while also learning an actual leading;

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Latest Advances In Inductive Logic Programming

for work on logic-based machine learning. The chapters cover a wide variety of topics, ranging from theory and ILP implementations to state;

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Pathway Modeling & Algorithm Research

learning approaches; pathway modelling; shortest path algorithms; clustering algorithms; inductive logic programming and computationally efficient;

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Representation Learning

) propositionalization approaches, established in relational learning and inductive logic programming, and (ii) embedding approaches, which have gained;

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Representation Learning

) propositionalization approaches, established in relational learning and inductive logic programming, and (ii) embedding approaches, which have gained;

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Statistical Relational Artificial Intelligence

and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models;

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Statistical Relational Artificial Intelligence

and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models;

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Statistical Relational Artificial Intelligence

and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models;

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Programming Logic and Design, Introductory

Discover the key principles necessary to develop structured program logic with Farrell's PROGRAMMING LOGIC AND DESIGN, INTRODUCTORY, 8E;

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