Logic for Learning: Learning Comprehensible Theories from Structured Data
This book is concerned with the rich and fruitful interplay between the fields of computational logic and machine learning. The intended audience is senior undergraduates, graduate students, and researchers in either of those fields. For those in computational logic, no previous knowledge of machine...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Berlin, Heidelberg
Springer Berlin Heidelberg
2003
|
| Ausgabe: | 1st ed. 2003 |
| Schriftenreihe: | Cognitive Technologies
|
| Schlagworte: | |
| Online-Zugang: | UBY01 Volltext |
| Zusammenfassung: | This book is concerned with the rich and fruitful interplay between the fields of computational logic and machine learning. The intended audience is senior undergraduates, graduate students, and researchers in either of those fields. For those in computational logic, no previous knowledge of machine learning is assumed and, for those in machine learning, no previous knowledge of computational logic is assumed. The logic used throughout the book is a higher-order one. Higher-order logic is already heavily used in some parts of computer science, for example, theoretical computer science, functional programming, and hardware verifica tion, mainly because of its great expressive power. Similar motivations apply here as well: higher-order functions can have other functions as arguments and this capability can be exploited to provide abstractions for knowledge representation, methods for constructing predicates, and a foundation for logic-based computation. The book should be of interest to researchers in machine learning, espe cially those who study learning methods for structured data. Machine learn ing applications are becoming increasingly concerned with applications for which the individuals that are the subject of learning have complex struc ture. Typical applications include text learning for the World Wide Web and bioinformatics. Traditional methods for such applications usually involve the extraction of features to reduce the problem to one of attribute-value learning |
| Beschreibung: | 1 Online-Ressource (X, 257 p) |
| ISBN: | 9783662084069 |
| DOI: | 10.1007/978-3-662-08406-9 |
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Datensatz im Suchindex
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| discipline | Informatik |
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| edition | 1st ed. 2003 |
| format | Electronic eBook |
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| index_date | 2024-07-03T16:12:23Z |
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| spelling | Lloyd, John W. Verfasser aut Logic for Learning Learning Comprehensible Theories from Structured Data by John W. Lloyd 1st ed. 2003 Berlin, Heidelberg Springer Berlin Heidelberg 2003 1 Online-Ressource (X, 257 p) txt rdacontent c rdamedia cr rdacarrier Cognitive Technologies This book is concerned with the rich and fruitful interplay between the fields of computational logic and machine learning. The intended audience is senior undergraduates, graduate students, and researchers in either of those fields. For those in computational logic, no previous knowledge of machine learning is assumed and, for those in machine learning, no previous knowledge of computational logic is assumed. The logic used throughout the book is a higher-order one. Higher-order logic is already heavily used in some parts of computer science, for example, theoretical computer science, functional programming, and hardware verifica tion, mainly because of its great expressive power. Similar motivations apply here as well: higher-order functions can have other functions as arguments and this capability can be exploited to provide abstractions for knowledge representation, methods for constructing predicates, and a foundation for logic-based computation. The book should be of interest to researchers in machine learning, espe cially those who study learning methods for structured data. Machine learn ing applications are becoming increasingly concerned with applications for which the individuals that are the subject of learning have complex struc ture. Typical applications include text learning for the World Wide Web and bioinformatics. Traditional methods for such applications usually involve the extraction of features to reduce the problem to one of attribute-value learning Artificial Intelligence Theory of Computation Data Structures and Information Theory Artificial intelligence Computers Data structures (Computer science) Funktionale Programmierung (DE-588)4198740-8 gnd rswk-swf Ordnung n (DE-588)4322729-6 gnd rswk-swf Logik (DE-588)4036202-4 gnd rswk-swf Algorithmische Lerntheorie (DE-588)4701014-9 gnd rswk-swf Logische Programmierung (DE-588)4195096-3 gnd rswk-swf Strukturierte Daten (DE-588)4620514-7 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Computational logic (DE-588)4255672-7 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Logik (DE-588)4036202-4 s Ordnung n (DE-588)4322729-6 s DE-604 Logische Programmierung (DE-588)4195096-3 s Funktionale Programmierung (DE-588)4198740-8 s Computational logic (DE-588)4255672-7 s Strukturierte Daten (DE-588)4620514-7 s Algorithmische Lerntheorie (DE-588)4701014-9 s Erscheint auch als Druck-Ausgabe 9783642075537 Erscheint auch als Druck-Ausgabe 9783540420279 Erscheint auch als Druck-Ausgabe 9783662084076 https://doi.org/10.1007/978-3-662-08406-9 Verlag URL des Eerstveröffentlichers Volltext |
| spellingShingle | Lloyd, John W. Logic for Learning Learning Comprehensible Theories from Structured Data Artificial Intelligence Theory of Computation Data Structures and Information Theory Artificial intelligence Computers Data structures (Computer science) Funktionale Programmierung (DE-588)4198740-8 gnd Ordnung n (DE-588)4322729-6 gnd Logik (DE-588)4036202-4 gnd Algorithmische Lerntheorie (DE-588)4701014-9 gnd Logische Programmierung (DE-588)4195096-3 gnd Strukturierte Daten (DE-588)4620514-7 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Computational logic (DE-588)4255672-7 gnd |
| subject_GND | (DE-588)4198740-8 (DE-588)4322729-6 (DE-588)4036202-4 (DE-588)4701014-9 (DE-588)4195096-3 (DE-588)4620514-7 (DE-588)4193754-5 (DE-588)4255672-7 |
| title | Logic for Learning Learning Comprehensible Theories from Structured Data |
| title_auth | Logic for Learning Learning Comprehensible Theories from Structured Data |
| title_exact_search | Logic for Learning Learning Comprehensible Theories from Structured Data |
| title_exact_search_txtP | Logic for Learning Learning Comprehensible Theories from Structured Data |
| title_full | Logic for Learning Learning Comprehensible Theories from Structured Data by John W. Lloyd |
| title_fullStr | Logic for Learning Learning Comprehensible Theories from Structured Data by John W. Lloyd |
| title_full_unstemmed | Logic for Learning Learning Comprehensible Theories from Structured Data by John W. Lloyd |
| title_short | Logic for Learning |
| title_sort | logic for learning learning comprehensible theories from structured data |
| title_sub | Learning Comprehensible Theories from Structured Data |
| topic | Artificial Intelligence Theory of Computation Data Structures and Information Theory Artificial intelligence Computers Data structures (Computer science) Funktionale Programmierung (DE-588)4198740-8 gnd Ordnung n (DE-588)4322729-6 gnd Logik (DE-588)4036202-4 gnd Algorithmische Lerntheorie (DE-588)4701014-9 gnd Logische Programmierung (DE-588)4195096-3 gnd Strukturierte Daten (DE-588)4620514-7 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Computational logic (DE-588)4255672-7 gnd |
| topic_facet | Artificial Intelligence Theory of Computation Data Structures and Information Theory Artificial intelligence Computers Data structures (Computer science) Funktionale Programmierung Ordnung n Logik Algorithmische Lerntheorie Logische Programmierung Strukturierte Daten Maschinelles Lernen Computational logic |
| url | https://doi.org/10.1007/978-3-662-08406-9 |
| work_keys_str_mv | AT lloydjohnw logicforlearninglearningcomprehensibletheoriesfromstructureddata |