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The state of the art of the bioengineering aspects of the morphology of microorganisms and their relationship to process performance are described in this volume. Materials and methods of the digital image analysis and mathematical modeling of hyphal elongation, branching and pellet formation as well as their application to various fungi and actinomycetes during the production of antibiotics and enzymes are presented.
Book Synopsis Foundations of Inductive Logic Programming by : Shan-Hwei Nienhuys-Cheng
Download or read book Foundations of Inductive Logic Programming written by Shan-Hwei Nienhuys-Cheng and published by Springer Science & Business Media. This book was released on 1997-04-18 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: The state of the art of the bioengineering aspects of the morphology of microorganisms and their relationship to process performance are described in this volume. Materials and methods of the digital image analysis and mathematical modeling of hyphal elongation, branching and pellet formation as well as their application to various fungi and actinomycetes during the production of antibiotics and enzymes are presented.
Book Synopsis Foundations of Inductive Logic Programming by : Shan-Hwei Nienhuys-Cheng
Download or read book Foundations of Inductive Logic Programming written by Shan-Hwei Nienhuys-Cheng and published by . This book was released on 2014-01-15 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Inductive Logic Programming is a young and rapidly growing field combining machine learning and logic programming. This self-contained tutorial is the first theoretical introduction to ILP; it provides the reader with a rigorous and sufficiently broad basis for future research in the area. In the first part, a thorough treatment of first-order logic, resolution-based theorem proving, and logic programming is given. The second part introduces the main concepts of ILP and systematically develops the most important results on model inference, inverse resolution, unfolding, refinement operators, least generalizations, and ways to deal with background knowledge. Furthermore, the authors give an overview of PAC learning results in ILP and of some of the most relevant implemented systems.
Book Synopsis Foundations of Inductive Logic Programming by : Shan-Hwei Nienhuys-Cheng
Download or read book Foundations of Inductive Logic Programming written by Shan-Hwei Nienhuys-Cheng and published by . This book was released on 1997 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Inductive Logic Programming is a young and rapidly growing field combining machine learning and logic programming. This self-contained tutorial is the first theoretical introduction to ILP; it provides the reader with a rigorous and sufficiently broad basis for future research in the area. In the first part, a thorough treatment of first-order logic, resolution-based theorem proving, and logic programming is given. The second part introduces the main concepts of ILP and systematically develops the most important results on model inference, inverse resolution, unfolding, refinement operators, least generalizations, and ways to deal with background knowledge. Furthermore, the authors give an overview of PAC learning results in ILP and of some of the most relevant implemented systems.
Book Synopsis Foundations of Inductive Logic Programming by : Shan-Hwei Nienhuys-Cheng
Download or read book Foundations of Inductive Logic Programming written by Shan-Hwei Nienhuys-Cheng and published by . This book was released on 1998 with total page 57 pages. Available in PDF, EPUB and Kindle. Book excerpt:
This book provides an introduction to probabilistic inductive logic programming. It places emphasis on the methods based on logic programming principles and covers formalisms and systems, implementations and applications, as well as theory.
Book Synopsis Probabilistic Inductive Logic Programming by : Luc De Raedt
Download or read book Probabilistic Inductive Logic Programming written by Luc De Raedt and published by Springer. This book was released on 2008-02-26 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an introduction to probabilistic inductive logic programming. It places emphasis on the methods based on logic programming principles and covers formalisms and systems, implementations and applications, as well as theory.
This book constitutes the strictly refereed post-workshop proceedings of the 6th International Workshop on Inductive Logic Programming, ILP-96, held in Stockholm, Sweden, in August 1996. The 21 full papers were carefully reviewed and selected for inclusion in the book in revised version. Also included is the invited contribution "Inductive logic programming for natural language processing" by Raymond J. Mooney. Among the topics covered are natural language learning, drug design, NMR and ECG analysis, glaucoma diagnosis, efficiency measures for implementations and database interaction, program synthesis, proof encoding and learning in the absence of negative data, and least generalizations under implication ordering.
Book Synopsis Inductive Logic Programming by : Stephen Muggleton
Download or read book Inductive Logic Programming written by Stephen Muggleton and published by Boom Koninklijke Uitgevers. This book was released on 1997-09-03 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the strictly refereed post-workshop proceedings of the 6th International Workshop on Inductive Logic Programming, ILP-96, held in Stockholm, Sweden, in August 1996. The 21 full papers were carefully reviewed and selected for inclusion in the book in revised version. Also included is the invited contribution "Inductive logic programming for natural language processing" by Raymond J. Mooney. Among the topics covered are natural language learning, drug design, NMR and ECG analysis, glaucoma diagnosis, efficiency measures for implementations and database interaction, program synthesis, proof encoding and learning in the absence of negative data, and least generalizations under implication ordering.
What Is Inductive Logic Programming A subfield of symbolic artificial intelligence known as inductive logic programming (ILP) use logic programming as a consistent representation for examples, background knowledge, and hypotheses. An ILP system will develop a hypothesised logic program in the event that it is provided with an encoding of the known background knowledge and a collection of examples that are represented as a logical database of facts. This program will involve all of the positive examples and none of the negative instances.In this model, the hypothesis is derived from positive instances, negative examples, and background knowledge. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Inductive Logic Programming Chapter 2: Stephen Muggleton Chapter 3: Progol Chapter 4: Program Synthesis Chapter 5: Inductive Programming Chapter 6: First-Order Logic Chapter 7: List of Rules of Inference Chapter 8: Disjunctive Normal Form Chapter 9: Resolution (Logic) Chapter 10: Answer Set Programming (II) Answering the public top questions about inductive logic programming. (III) Real world examples for the usage of inductive logic programming in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of inductive logic programming' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of inductive logic programming.
Book Synopsis Inductive Logic Programming by : Fouad Sabry
Download or read book Inductive Logic Programming written by Fouad Sabry and published by One Billion Knowledgeable. This book was released on 2023-06-30 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: What Is Inductive Logic Programming A subfield of symbolic artificial intelligence known as inductive logic programming (ILP) use logic programming as a consistent representation for examples, background knowledge, and hypotheses. An ILP system will develop a hypothesised logic program in the event that it is provided with an encoding of the known background knowledge and a collection of examples that are represented as a logical database of facts. This program will involve all of the positive examples and none of the negative instances.In this model, the hypothesis is derived from positive instances, negative examples, and background knowledge. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Inductive Logic Programming Chapter 2: Stephen Muggleton Chapter 3: Progol Chapter 4: Program Synthesis Chapter 5: Inductive Programming Chapter 6: First-Order Logic Chapter 7: List of Rules of Inference Chapter 8: Disjunctive Normal Form Chapter 9: Resolution (Logic) Chapter 10: Answer Set Programming (II) Answering the public top questions about inductive logic programming. (III) Real world examples for the usage of inductive logic programming in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of inductive logic programming' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of inductive logic programming.
Probabilistic Logic Programming extends Logic Programming by enabling the representation of uncertain information. Probabilistic Logic Programming is at the intersection of two wider research fields: the integration of logic and probability and Probabilistic Programming. Logic enables the representation of complex relations among entities while probability theory is useful for model uncertainty over attributes and relations. Combining the two is a very active field of study. Probabilistic Programming extends programming languages with probabilistic primitives that can be used to write complex probabilistic models. Algorithms for the inference and learning tasks are then provided automatically by the system. Probabilistic Logic programming is at the same time a logic language, with its knowledge representation capabilities, and a Turing complete language, with its computation capabilities, thus providing the best of both worlds. Since its birth, the field of Probabilistic Logic Programming has seen a steady increase of activity, with many proposals for languages and algorithms for inference and learning. Foundations of Probabilistic Logic Programming aims at providing an overview of the field with a special emphasis on languages under the Distribution Semantics, one of the most influential approaches. The book presents the main ideas for semantics, inference, and learning and highlights connections between the methods. Many examples of the book include a link to a page of the web application http://cplint.eu where the code can be run online.
Book Synopsis Foundations of Probabilistic Logic Programming by : Fabrizio Riguzzi
Download or read book Foundations of Probabilistic Logic Programming written by Fabrizio Riguzzi and published by River Publishers. This book was released on 2018-09-01 with total page 422 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic Logic Programming extends Logic Programming by enabling the representation of uncertain information. Probabilistic Logic Programming is at the intersection of two wider research fields: the integration of logic and probability and Probabilistic Programming. Logic enables the representation of complex relations among entities while probability theory is useful for model uncertainty over attributes and relations. Combining the two is a very active field of study. Probabilistic Programming extends programming languages with probabilistic primitives that can be used to write complex probabilistic models. Algorithms for the inference and learning tasks are then provided automatically by the system. Probabilistic Logic programming is at the same time a logic language, with its knowledge representation capabilities, and a Turing complete language, with its computation capabilities, thus providing the best of both worlds. Since its birth, the field of Probabilistic Logic Programming has seen a steady increase of activity, with many proposals for languages and algorithms for inference and learning. Foundations of Probabilistic Logic Programming aims at providing an overview of the field with a special emphasis on languages under the Distribution Semantics, one of the most influential approaches. The book presents the main ideas for semantics, inference, and learning and highlights connections between the methods. Many examples of the book include a link to a page of the web application http://cplint.eu where the code can be run online.
This book constitutes the thoroughly refereed post-proceedings of the 22nd International Conference on Inductive Logic Programming, ILP 2012, held in Dubrovnik, Croatia, in September 2012. The 18 revised full papers were carefully reviewed and selected from 41 submissions. The papers cover the following topics: propositionalization, logical foundations, implementations, probabilistic ILP, applications in robotics and biology, grammatical inference, spatial learning and graph-based learning.
Book Synopsis Inductive Logic Programming by : Fabrizio Riguzzi
Download or read book Inductive Logic Programming written by Fabrizio Riguzzi and published by Springer. This book was released on 2013-06-04 with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-proceedings of the 22nd International Conference on Inductive Logic Programming, ILP 2012, held in Dubrovnik, Croatia, in September 2012. The 18 revised full papers were carefully reviewed and selected from 41 submissions. The papers cover the following topics: propositionalization, logical foundations, implementations, probabilistic ILP, applications in robotics and biology, grammatical inference, spatial learning and graph-based learning.
Rules – the clearest, most explored and best understood form of knowledge representation – are particularly important for data mining, as they offer the best tradeoff between human and machine understandability. This book presents the fundamentals of rule learning as investigated in classical machine learning and modern data mining. It introduces a feature-based view, as a unifying framework for propositional and relational rule learning, thus bridging the gap between attribute-value learning and inductive logic programming, and providing complete coverage of most important elements of rule learning. The book can be used as a textbook for teaching machine learning, as well as a comprehensive reference to research in the field of inductive rule learning. As such, it targets students, researchers and developers of rule learning algorithms, presenting the fundamental rule learning concepts in sufficient breadth and depth to enable the reader to understand, develop and apply rule learning techniques to real-world data.
Book Synopsis Foundations of Rule Learning by : Johannes Fürnkranz
Download or read book Foundations of Rule Learning written by Johannes Fürnkranz and published by Springer Science & Business Media. This book was released on 2012-11-06 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: Rules – the clearest, most explored and best understood form of knowledge representation – are particularly important for data mining, as they offer the best tradeoff between human and machine understandability. This book presents the fundamentals of rule learning as investigated in classical machine learning and modern data mining. It introduces a feature-based view, as a unifying framework for propositional and relational rule learning, thus bridging the gap between attribute-value learning and inductive logic programming, and providing complete coverage of most important elements of rule learning. The book can be used as a textbook for teaching machine learning, as well as a comprehensive reference to research in the field of inductive rule learning. As such, it targets students, researchers and developers of rule learning algorithms, presenting the fundamental rule learning concepts in sufficient breadth and depth to enable the reader to understand, develop and apply rule learning techniques to real-world data.
