On learning systems
The class of learning systems under consideration uses generalized linear algorithms which evaluate the appropriate parameters after processing the arbitrary groups of data. The algorithms reported earlier are obtained as particular cases. The properties of generalized algorithms are found, optimal...
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| Published in | Automatica (Oxford) Vol. 8; no. 1; pp. 85 - 91 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
1972
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| Online Access | Get full text |
| ISSN | 0005-1098 1873-2836 |
| DOI | 10.1016/0005-1098(72)90012-X |
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| Abstract | The class of learning systems under consideration uses generalized linear algorithms which evaluate the appropriate parameters after processing the arbitrary groups of data.
The algorithms reported earlier are obtained as particular cases. The properties of generalized algorithms are found, optimal algorithms are determined. The algorithms are used in construction of learning systems intended to estimate the characteristics of random sequences, for pattern recognition, communication, etc.
La catégorie considérée de systèmes à apprentissage emploie des algorithmes linéaires généralisés qui évaluent les paramètres appropriés après traitement de groupes arbitraires de données.
Les algorithmes mentionnés plus tôt sont obtenus comme des cas particuliers. Les proprietés des algorithmes généralisés sont trouvées, les algorithmes optimaux sont determinés. Les algorithmes sont employés pour construire des systèmes à apprentissage destinés à estimer les caractéristiques de séquences aléatoires pour la reconnaissance des formes, les communications etc.
Bei der betrachteten Klasse lernender Systeme werden verallgemeinerte lineare Algorithmen, die nach der Verarbeitung beliebiger Datengruppen geeignete Parameterwerte vermitteln, benutzt.
Die früher angegebenen Algorithmen wurden als Spezialfälle erhalten. Die Eigenschaften verallgemeinerter Algorithmen wurden gefunden und optimale Algorithmen bestimmt. Die Algorithmen werden bei der Konstruktion von Lernsystemen benutzt, die dazu bestimmt sind, um die Charakteristiken von Zufallsfolgen für die Zeichenerkennung, Kommunikation usw. zu erhalten.
Paccмaтpивaeмый клacc caмooбyчaющичcя cиcтeм иcпoльзyeт oбoбщeнныe линeйныe aлгopитмы кoтopыe oцeнивaют cooтвeтcтвyющиe пapaмeтpы пocлe oбpaбoтки cлyчaйныч гpyпп дaнныч.
Укaзaнныe paнee aлгopитмы пoлчyeны кaк чacтныe cлyчaи. Haйдeны cвoйcтвa oбoбщeнныч aлгopитмoв, oпpeдeлeны oптимaльныe aлгopитмы. Aлгopитмы иcпoльзyютcя для кoнcтpyиpoвaния caмooбyчaющичcя cиcтeм имeющич цeлью oцeнкy чapaктepиcтик cлyчaйныч пocлeдoвaтeльнocтeй для pacпoзнaвaния oбpaзoв, cвязи и т.д. |
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| AbstractList | The class of learning systems under consideration uses generalized linear algorithms which evaluate the appropriate parameters after processing the arbitrary groups of data.
The algorithms reported earlier are obtained as particular cases. The properties of generalized algorithms are found, optimal algorithms are determined. The algorithms are used in construction of learning systems intended to estimate the characteristics of random sequences, for pattern recognition, communication, etc.
La catégorie considérée de systèmes à apprentissage emploie des algorithmes linéaires généralisés qui évaluent les paramètres appropriés après traitement de groupes arbitraires de données.
Les algorithmes mentionnés plus tôt sont obtenus comme des cas particuliers. Les proprietés des algorithmes généralisés sont trouvées, les algorithmes optimaux sont determinés. Les algorithmes sont employés pour construire des systèmes à apprentissage destinés à estimer les caractéristiques de séquences aléatoires pour la reconnaissance des formes, les communications etc.
Bei der betrachteten Klasse lernender Systeme werden verallgemeinerte lineare Algorithmen, die nach der Verarbeitung beliebiger Datengruppen geeignete Parameterwerte vermitteln, benutzt.
Die früher angegebenen Algorithmen wurden als Spezialfälle erhalten. Die Eigenschaften verallgemeinerter Algorithmen wurden gefunden und optimale Algorithmen bestimmt. Die Algorithmen werden bei der Konstruktion von Lernsystemen benutzt, die dazu bestimmt sind, um die Charakteristiken von Zufallsfolgen für die Zeichenerkennung, Kommunikation usw. zu erhalten.
Paccмaтpивaeмый клacc caмooбyчaющичcя cиcтeм иcпoльзyeт oбoбщeнныe линeйныe aлгopитмы кoтopыe oцeнивaют cooтвeтcтвyющиe пapaмeтpы пocлe oбpaбoтки cлyчaйныч гpyпп дaнныч.
Укaзaнныe paнee aлгopитмы пoлчyeны кaк чacтныe cлyчaи. Haйдeны cвoйcтвa oбoбщeнныч aлгopитмoв, oпpeдeлeны oптимaльныe aлгopитмы. Aлгopитмы иcпoльзyютcя для кoнcтpyиpoвaния caмooбyчaющичcя cиcтeм имeющич цeлью oцeнкy чapaктepиcтик cлyчaйныч пocлeдoвaтeльнocтeй для pacпoзнaвaния oбpaзoв, cвязи и т.д. |
| Author | Tsykin, Ya S. |
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| References | Tsypkin (BIB4) 1967 Tsypkin (BIB1) 1968 Chien, Fu (BIB5) 1967; SSC-3 Nicolic, Fu (BIB3) 1966; 22 Schalkwijk, Kailath (BIB12) 1966; IT-12 Dvoretsky (BIB2) 1956; Vol. 1 Bush, Mosteller (BIB8) 1960 Tsypkin (BIB6) 1968 Fu (BIB7) 1967; Vol. II Tsetlin (BIB10) 1961; 22 Varshavskii, Vorontsova (BIB11) 1963; 24 Tsetlin (BIB9) 1961; 00 Tsypkin (10.1016/0005-1098(72)90012-X_BIB1) 1968 Tsypkin (10.1016/0005-1098(72)90012-X_BIB4) 1967 Chien (10.1016/0005-1098(72)90012-X_BIB5) 1967; SSC-3 Nicolic (10.1016/0005-1098(72)90012-X_BIB3) 1966; 22 Bush (10.1016/0005-1098(72)90012-X_BIB8) 1960 Varshavskii (10.1016/0005-1098(72)90012-X_BIB11) 1963; 24 Tsetlin (10.1016/0005-1098(72)90012-X_BIB9) 1961; 00 Fu (10.1016/0005-1098(72)90012-X_BIB7) 1967; Vol. II Dvoretsky (10.1016/0005-1098(72)90012-X_BIB2) 1956; Vol. 1 Tsypkin (10.1016/0005-1098(72)90012-X_BIB6) 1968 Schalkwijk (10.1016/0005-1098(72)90012-X_BIB12) 1966; IT-12 Tsetlin (10.1016/0005-1098(72)90012-X_BIB10) 1961; 22 |
| References_xml | – year: 1960 ident: BIB8 article-title: Stochastic Models for Learning – volume: 00 year: 1961 ident: BIB9 article-title: Certain problems on the behavior of finite automata publication-title: Dok Akadl. Nauk SSSR – volume: 22 year: 1961 ident: BIB10 article-title: On the behavior of finite automata in random environments publication-title: Automatika i Telemekhanika – volume: 22 year: 1966 ident: BIB3 article-title: A mathematical model of learning in an unknown random environment publication-title: Proc. N.E.C – year: 1968 ident: BIB6 article-title: After all, does there really exist a synthesis theory for optimal adaptive systems? publication-title: Automatika i Telemekhanika – volume: Vol. II year: 1967 ident: BIB7 article-title: Stochastic automata as models of learning systems publication-title: Computer & Information Sciences – volume: 24 year: 1963 ident: BIB11 article-title: On the behavior of stochastic automata with variable structure publication-title: Automatika i Telemekhanika – year: 1968 ident: BIB1 article-title: Adaptation and learning in control systems publication-title: Nauka, Moscow – year: 1967 ident: BIB4 article-title: On algorithms for the evaluation of the probability densities and the moments by observations publication-title: Automatika i Telemekhanika – volume: SSC-3 year: 1967 ident: BIB5 article-title: On Bayesian learning and stochastic approximation publication-title: IEEE Trans. Sci. Cybernet – volume: IT-12 year: 1966 ident: BIB12 article-title: A coding scheme for additive noise channels with feedback. Parts I and II publication-title: IEEE Trans. Inform. Theory – volume: Vol. 1 year: 1956 ident: BIB2 article-title: On stochastic approximations publication-title: Proc. Third Berkeley Sympos. Math. Statist. Prob – volume: Vol. II year: 1967 ident: 10.1016/0005-1098(72)90012-X_BIB7 article-title: Stochastic automata as models of learning systems – issue: No. 7 year: 1967 ident: 10.1016/0005-1098(72)90012-X_BIB4 article-title: On algorithms for the evaluation of the probability densities and the moments by observations publication-title: Automatika i Telemekhanika – volume: 22 year: 1966 ident: 10.1016/0005-1098(72)90012-X_BIB3 article-title: A mathematical model of learning in an unknown random environment – volume: 00 issue: No. 4 year: 1961 ident: 10.1016/0005-1098(72)90012-X_BIB9 article-title: Certain problems on the behavior of finite automata publication-title: Dok Akadl. Nauk SSSR – volume: 24 issue: No. 3 year: 1963 ident: 10.1016/0005-1098(72)90012-X_BIB11 article-title: On the behavior of stochastic automata with variable structure publication-title: Automatika i Telemekhanika – volume: Vol. 1 year: 1956 ident: 10.1016/0005-1098(72)90012-X_BIB2 article-title: On stochastic approximations – year: 1960 ident: 10.1016/0005-1098(72)90012-X_BIB8 – volume: SSC-3 issue: No. 1 year: 1967 ident: 10.1016/0005-1098(72)90012-X_BIB5 article-title: On Bayesian learning and stochastic approximation publication-title: IEEE Trans. Sci. Cybernet – issue: No. 1 year: 1968 ident: 10.1016/0005-1098(72)90012-X_BIB6 article-title: After all, does there really exist a synthesis theory for optimal adaptive systems? publication-title: Automatika i Telemekhanika – volume: 22 issue: No. 10 year: 1961 ident: 10.1016/0005-1098(72)90012-X_BIB10 article-title: On the behavior of finite automata in random environments publication-title: Automatika i Telemekhanika – year: 1968 ident: 10.1016/0005-1098(72)90012-X_BIB1 article-title: Adaptation and learning in control systems publication-title: Nauka, Moscow – volume: IT-12 issue: No. 2 year: 1966 ident: 10.1016/0005-1098(72)90012-X_BIB12 article-title: A coding scheme for additive noise channels with feedback. Parts I and II publication-title: IEEE Trans. Inform. Theory |
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