Markov Logic (Record no. 84804)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03482nam a22005295i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-031-01549-6 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240730163625.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 220601s2009 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783031015496 |
| -- | 978-3-031-01549-6 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006.3 |
| 100 1# - AUTHOR NAME | |
| Author | Domingos, Pedro. |
| 245 10 - TITLE STATEMENT | |
| Title | Markov Logic |
| Sub Title | An Interface Layer for Artificial Intelligence / |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2009. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | IX, 145 p. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Synthesis Lectures on Artificial Intelligence and Machine Learning, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Introduction -- Markov Logic -- Inference -- Learning -- Extensions -- Applications -- Conclusion. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Most subfields of computer science have an interface layer via which applications communicate with the infrastructure, and this is key to their success (e.g., the Internet in networking, the relational model in databases, etc.). So far this interface layer has been missing in AI. First-order logic and probabilistic graphical models each have some of the necessary features, but a viable interface layer requires combining both. Markov logic is a powerful new language that accomplishes this by attaching weights to first-order formulas and treating them as templates for features of Markov random fields. Most statistical models in wide use are special cases of Markov logic, and first-order logic is its infinite-weight limit. Inference algorithms for Markov logic combine ideas from satisfiability, Markov chain Monte Carlo, belief propagation, and resolution. Learning algorithms make use of conditional likelihood, convex optimization, and inductive logic programming. Markov logic has been successfully applied to problems in information extraction and integration, natural language processing, robot mapping, social networks, computational biology, and others, and is the basis of the open-source Alchemy system. Table of Contents: Introduction / Markov Logic / Inference / Learning / Extensions / Applications / Conclusion. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Lowd, Daniel. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-3-031-01549-6 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2009. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
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| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
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| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Machine learning. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Neural networks (Computer science) . |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial Intelligence. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Machine Learning. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Mathematical Models of Cognitive Processes and Neural Networks. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 1939-4616 |
| 912 ## - | |
| -- | ZDB-2-SXSC |
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