Zobrazeno 1 - 10
of 75
pro vyhledávání: '"Vladimir Vapnik"'
Autor:
Vladimir Vapnik
Publikováno v:
Automation and Remote Control. 80:1949-1975
Existing mathematical model of learning requires using training data find in a given subset of admissible function the function that minimizes the expected loss. In the paper this setting is called Second selection problem. Mathematical model of lear
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b54221e7687e07565dc7ea3e6f9103bd
https://doi.org/10.1134/s000511791911002x
https://doi.org/10.1134/s000511791911002x
Autor:
Vladimir Vapnik, Rauf Izmailov
Publikováno v:
Machine Learning. 108:381-423
This paper introduces a new learning paradigm, called Learning Using Statistical Invariants (LUSI), which is different from the classical one. In a classical paradigm, the learning machine constructs a classification rule that minimizes the probabili
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2403c8c10896635fd2152f3059497313
https://doi.org/10.1007/s10994-018-5742-0
https://doi.org/10.1007/s10994-018-5742-0
Autor:
Vladimir Vapnik, Rauf Izmailov
Publikováno v:
Pattern Recognition. 119:108018
The paper is devoted to two problems: (1) reinforcement of SVM algorithms, and (2) justification of memorization mechanisms for generalization. (1) Current SVM algorithm was designed for the case when the risk for the set of nonnegative slack variabl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::34cd50618380ab9c3feaba9fb3305deb
https://doi.org/10.1016/j.patcog.2021.108018
https://doi.org/10.1016/j.patcog.2021.108018
Autor:
Rauf Izmailov, Vladimir Vapnik
Publikováno v:
Annals of Mathematics and Artificial Intelligence. 81:3-19
The paper considers general machine learning models, where knowledge transfer is positioned as the main method to improve their convergence properties. Previous research was focused on mechanisms of knowledge transfer in the context of SVM framework;
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::077fdea6c8d0e6f7eba44861940e0e9b
https://doi.org/10.1007/s10472-017-9538-x
https://doi.org/10.1007/s10472-017-9538-x
Publikováno v:
Statistical Analysis and Data Mining: The ASA Data Science Journal. 8:137-146
We introduce a constructive setting for the problem of density ratio estimation through the solution of a multidimensional integral equation. In this equation, not only its right hand side is approximately known, but also the integral operator is app
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f6ca4e0aeef4495d0c3d98ef48e01016
https://doi.org/10.1002/sam.11263
https://doi.org/10.1002/sam.11263
Autor:
Vladimir Vapnik, Rauf Izmailov
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783319333946
COPA
COPA
The paper considers several topics on learning with privileged information: 1 general machine learning models, where privileged information is positioned as the main mechanism to improve their convergence properties, 2 existing and novel approaches t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::70652da4b0af411ce3351ce00f3178ce
https://doi.org/10.1007/978-3-319-33395-3_1
https://doi.org/10.1007/978-3-319-33395-3_1
Autor:
Akshay Vashist, Vladimir Vapnik
Publikováno v:
Neural Networks. 22:544-557
In the Afterword to the second edition of the book ''Estimation of Dependences Based on Empirical Data'' by V. Vapnik, an advanced learning paradigm called Learning Using Hidden Information (LUHI) was introduced. This Afterword also suggested an exte
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::64266aebb8289399ad2e34f7bfe4bd13
https://doi.org/10.1016/j.neunet.2009.06.042
https://doi.org/10.1016/j.neunet.2009.06.042
Autor:
Xiao Liu, David Bell, Alexey Ya. Chervonenkis, Glenn Shafer, Sally McClean, Alan Hutchinson, David L. Dowe, Philip M. Long, Zhiyuan Luo, Tony Bellotti, Drago Indjic, Harris Papadopoulos, G.I. Hawe, Vladimir Vapnik
Publikováno v:
The Computer Journal. 50:164-172
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fe74ea2700c691d597f0e0c089b61b08
https://doi.org/10.1093/comjnl/bxl066
https://doi.org/10.1093/comjnl/bxl066
Autor:
Vladimir Vapnik, Rauf Izmailov
Publikováno v:
Statistical Learning and Data Sciences ISBN: 9783319170909
This paper presents direct settings and rigorous solutions of Statistical Inference problems. It shows that rigorous solutions require solving ill-posed Fredholm integral equations of the first kind in the situation where not only the right-hand side
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9cf2ee19cdd479bdd5b3db031bed6608
https://doi.org/10.1007/978-3-319-17091-6_2
https://doi.org/10.1007/978-3-319-17091-6_2
Autor:
A. Ya. Chervonenkis, Vladimir Vapnik
Publikováno v:
Measures of Complexity ISBN: 9783319218519
This chapter reproduces the English translation by B. Seckler of the paper by Vapnik and Chervonenkis in which they gave proofs for the innovative results they had obtained in a draft form in July 1966 and announced in 1968 in their note in Soviet Ma
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::95fcd1c5a732cc21a5d2f3cf34a68214
https://doi.org/10.1007/978-3-319-21852-6_3
https://doi.org/10.1007/978-3-319-21852-6_3