Learning Algorithms Theory and Applications:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer New York
1981
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | Learning constitutes one of the most important phase of the whole psychological processes and it is essential in many ways for the occurrence of necessary changes in the behavior of adjusting organisms. In a broad sense influence of prior behavior and its consequence upon subsequent behavior is usually accepted as a definition of learning. Till recently learning was regarded as the prerogative of living beings. But in the past few decades there have been attempts to construct learning machines or systems with considerable success. This book deals with a powerful class of learning algorithms that have been developed over the past two decades in the context of learning systems modelled by finite state probabilistic automaton. These algorithms are very simple iterative schemes. Mathematically these algorithms define two distinct classes of Markov processes with unit simplex (of suitable dimension) as its state space. The basic problem of learning is viewed as one of finding conditions on the algorithm such that the associated Markov process has prespecified asymptotic behavior. As a prerequisite a first course in analysis and stochastic processes would be an adequate preparation to pursue the development in various chapters |
| Beschreibung: | 1 Online-Ressource (XII, 280 p) |
| ISBN: | 9781461259756 9780387906409 |
| DOI: | 10.1007/978-1-4612-5975-6 |
Internformat
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| 500 | |a Learning constitutes one of the most important phase of the whole psychological processes and it is essential in many ways for the occurrence of necessary changes in the behavior of adjusting organisms. In a broad sense influence of prior behavior and its consequence upon subsequent behavior is usually accepted as a definition of learning. Till recently learning was regarded as the prerogative of living beings. But in the past few decades there have been attempts to construct learning machines or systems with considerable success. This book deals with a powerful class of learning algorithms that have been developed over the past two decades in the context of learning systems modelled by finite state probabilistic automaton. These algorithms are very simple iterative schemes. Mathematically these algorithms define two distinct classes of Markov processes with unit simplex (of suitable dimension) as its state space. The basic problem of learning is viewed as one of finding conditions on the algorithm such that the associated Markov process has prespecified asymptotic behavior. As a prerequisite a first course in analysis and stochastic processes would be an adequate preparation to pursue the development in various chapters | ||
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Datensatz im Suchindex
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| any_adam_object | |
| author | Lakshmivarahan, S. |
| author_facet | Lakshmivarahan, S. |
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| building | Verbundindex |
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| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 518 - Numerical analysis |
| dewey-raw | 518 |
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| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| doi_str_mv | 10.1007/978-1-4612-5975-6 |
| format | Electronic eBook |
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| indexdate | 2024-07-10T01:21:06Z |
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| isbn | 9781461259756 9780387906409 |
| language | English |
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| spelling | Lakshmivarahan, S. Verfasser aut Learning Algorithms Theory and Applications by S. Lakshmivarahan New York, NY Springer New York 1981 1 Online-Ressource (XII, 280 p) txt rdacontent c rdamedia cr rdacarrier Learning constitutes one of the most important phase of the whole psychological processes and it is essential in many ways for the occurrence of necessary changes in the behavior of adjusting organisms. In a broad sense influence of prior behavior and its consequence upon subsequent behavior is usually accepted as a definition of learning. Till recently learning was regarded as the prerogative of living beings. But in the past few decades there have been attempts to construct learning machines or systems with considerable success. This book deals with a powerful class of learning algorithms that have been developed over the past two decades in the context of learning systems modelled by finite state probabilistic automaton. These algorithms are very simple iterative schemes. Mathematically these algorithms define two distinct classes of Markov processes with unit simplex (of suitable dimension) as its state space. The basic problem of learning is viewed as one of finding conditions on the algorithm such that the associated Markov process has prespecified asymptotic behavior. As a prerequisite a first course in analysis and stochastic processes would be an adequate preparation to pursue the development in various chapters Mathematics Numerical analysis Numerical Analysis Mathematik Adaptivregelung (DE-588)4000457-0 gnd rswk-swf Spieltheorie (DE-588)4056243-8 gnd rswk-swf Informationstheorie (DE-588)4026927-9 gnd rswk-swf Algorithmentheorie (DE-588)4200409-3 gnd rswk-swf Algorithmus (DE-588)4001183-5 gnd rswk-swf Programmanalyse (DE-588)4175841-9 gnd rswk-swf Digitale Signalverarbeitung (DE-588)4113314-6 gnd rswk-swf Theorie (DE-588)4059787-8 gnd rswk-swf Algorithmus (DE-588)4001183-5 s Theorie (DE-588)4059787-8 s 1\p DE-604 Digitale Signalverarbeitung (DE-588)4113314-6 s 2\p DE-604 Programmanalyse (DE-588)4175841-9 s 3\p DE-604 Adaptivregelung (DE-588)4000457-0 s 4\p DE-604 Spieltheorie (DE-588)4056243-8 s 5\p DE-604 Informationstheorie (DE-588)4026927-9 s 6\p DE-604 Algorithmentheorie (DE-588)4200409-3 s 7\p DE-604 https://doi.org/10.1007/978-1-4612-5975-6 Verlag 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 3\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 4\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 5\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 6\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 7\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Lakshmivarahan, S. Learning Algorithms Theory and Applications Mathematics Numerical analysis Numerical Analysis Mathematik Adaptivregelung (DE-588)4000457-0 gnd Spieltheorie (DE-588)4056243-8 gnd Informationstheorie (DE-588)4026927-9 gnd Algorithmentheorie (DE-588)4200409-3 gnd Algorithmus (DE-588)4001183-5 gnd Programmanalyse (DE-588)4175841-9 gnd Digitale Signalverarbeitung (DE-588)4113314-6 gnd Theorie (DE-588)4059787-8 gnd |
| subject_GND | (DE-588)4000457-0 (DE-588)4056243-8 (DE-588)4026927-9 (DE-588)4200409-3 (DE-588)4001183-5 (DE-588)4175841-9 (DE-588)4113314-6 (DE-588)4059787-8 |
| title | Learning Algorithms Theory and Applications |
| title_auth | Learning Algorithms Theory and Applications |
| title_exact_search | Learning Algorithms Theory and Applications |
| title_full | Learning Algorithms Theory and Applications by S. Lakshmivarahan |
| title_fullStr | Learning Algorithms Theory and Applications by S. Lakshmivarahan |
| title_full_unstemmed | Learning Algorithms Theory and Applications by S. Lakshmivarahan |
| title_short | Learning Algorithms Theory and Applications |
| title_sort | learning algorithms theory and applications |
| topic | Mathematics Numerical analysis Numerical Analysis Mathematik Adaptivregelung (DE-588)4000457-0 gnd Spieltheorie (DE-588)4056243-8 gnd Informationstheorie (DE-588)4026927-9 gnd Algorithmentheorie (DE-588)4200409-3 gnd Algorithmus (DE-588)4001183-5 gnd Programmanalyse (DE-588)4175841-9 gnd Digitale Signalverarbeitung (DE-588)4113314-6 gnd Theorie (DE-588)4059787-8 gnd |
| topic_facet | Mathematics Numerical analysis Numerical Analysis Mathematik Adaptivregelung Spieltheorie Informationstheorie Algorithmentheorie Algorithmus Programmanalyse Digitale Signalverarbeitung Theorie |
| url | https://doi.org/10.1007/978-1-4612-5975-6 |
| work_keys_str_mv | AT lakshmivarahans learningalgorithmstheoryandapplications |