Incremental Version-Space Merging: A General Framework for Concept Learning:
One of the most enjoyable experiences in science is hearing a simple but novel idea which instantly rings true, and whose consequences then begin to unfold in unforeseen directions. For me, this book presents such an idea and several of its ramifications. This book is concerned with machine learning...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston, MA
Springer US
1990
|
| Schriftenreihe: | The Kluwer International Series in Engineering and Computer Science, Knowledge Representation, Learning and Expert Systems
104 |
| Schlagworte: | |
| Online-Zugang: | BTU01 URL des Erstveröffentlichers |
| Zusammenfassung: | One of the most enjoyable experiences in science is hearing a simple but novel idea which instantly rings true, and whose consequences then begin to unfold in unforeseen directions. For me, this book presents such an idea and several of its ramifications. This book is concerned with machine learning. It focuses on a ques tion that is central to understanding how computers might learn: "how can a computer acquire the definition of some general concept by abstracting from specific training instances of the concept?" Although this question of how to automatically generalize from examples has been considered by many researchers over several decades, it remains only partly answered. The approach developed in this book, based on Haym Hirsh's Ph.D. dis sertation, leads to an algorithm which efficiently and exhaustively searches a space of hypotheses (possible generalizations of the data) to find all maxi mally consistent hypotheses, even in the presence of certain types of incon sistencies in the data. More generally, it provides a framework for integrat ing different types of constraints (e.g., training examples, prior knowledge) which allow the learner to reduce the set of hypotheses under consideration |
| Beschreibung: | 1 Online-Ressource (XVI, 116 p) |
| ISBN: | 9781461315575 |
| DOI: | 10.1007/978-1-4613-1557-5 |
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| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3 |
| dewey-search | 006.3 |
| dewey-sort | 16.3 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| doi_str_mv | 10.1007/978-1-4613-1557-5 |
| format | Electronic eBook |
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| spelling | Hirsh, Haym Verfasser aut Incremental Version-Space Merging: A General Framework for Concept Learning by Haym Hirsh Boston, MA Springer US 1990 1 Online-Ressource (XVI, 116 p) txt rdacontent c rdamedia cr rdacarrier The Kluwer International Series in Engineering and Computer Science, Knowledge Representation, Learning and Expert Systems 104 One of the most enjoyable experiences in science is hearing a simple but novel idea which instantly rings true, and whose consequences then begin to unfold in unforeseen directions. For me, this book presents such an idea and several of its ramifications. This book is concerned with machine learning. It focuses on a ques tion that is central to understanding how computers might learn: "how can a computer acquire the definition of some general concept by abstracting from specific training instances of the concept?" Although this question of how to automatically generalize from examples has been considered by many researchers over several decades, it remains only partly answered. The approach developed in this book, based on Haym Hirsh's Ph.D. dis sertation, leads to an algorithm which efficiently and exhaustively searches a space of hypotheses (possible generalizations of the data) to find all maxi mally consistent hypotheses, even in the presence of certain types of incon sistencies in the data. More generally, it provides a framework for integrat ing different types of constraints (e.g., training examples, prior knowledge) which allow the learner to reduce the set of hypotheses under consideration Computer Science Artificial Intelligence (incl. Robotics) Computer science Artificial intelligence Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Kognition (DE-588)4031630-0 gnd rswk-swf 1\p (DE-588)4113937-9 Hochschulschrift gnd-content Künstliche Intelligenz (DE-588)4033447-8 s Kognition (DE-588)4031630-0 s 2\p DE-604 Erscheint auch als Druck-Ausgabe 9781461288343 https://doi.org/10.1007/978-1-4613-1557-5 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Hirsh, Haym Incremental Version-Space Merging: A General Framework for Concept Learning Computer Science Artificial Intelligence (incl. Robotics) Computer science Artificial intelligence Künstliche Intelligenz (DE-588)4033447-8 gnd Kognition (DE-588)4031630-0 gnd |
| subject_GND | (DE-588)4033447-8 (DE-588)4031630-0 (DE-588)4113937-9 |
| title | Incremental Version-Space Merging: A General Framework for Concept Learning |
| title_auth | Incremental Version-Space Merging: A General Framework for Concept Learning |
| title_exact_search | Incremental Version-Space Merging: A General Framework for Concept Learning |
| title_full | Incremental Version-Space Merging: A General Framework for Concept Learning by Haym Hirsh |
| title_fullStr | Incremental Version-Space Merging: A General Framework for Concept Learning by Haym Hirsh |
| title_full_unstemmed | Incremental Version-Space Merging: A General Framework for Concept Learning by Haym Hirsh |
| title_short | Incremental Version-Space Merging: A General Framework for Concept Learning |
| title_sort | incremental version space merging a general framework for concept learning |
| topic | Computer Science Artificial Intelligence (incl. Robotics) Computer science Artificial intelligence Künstliche Intelligenz (DE-588)4033447-8 gnd Kognition (DE-588)4031630-0 gnd |
| topic_facet | Computer Science Artificial Intelligence (incl. Robotics) Computer science Artificial intelligence Künstliche Intelligenz Kognition Hochschulschrift |
| url | https://doi.org/10.1007/978-1-4613-1557-5 |
| work_keys_str_mv | AT hirshhaym incrementalversionspacemergingageneralframeworkforconceptlearning |