Zobrazeno 1 - 10
of 101
pro vyhledávání: '"Littlestone, A."'
Autor:
Houston, Kevin, Littlestone, Daniel
Generators for the module of vector fields liftable over corank 1 stable complex analytic maps from an n-manifold to an (n+1)-manifold are found. This is applied to the classification of the singularities occuring in generic one-parameter families of
Externí odkaz:
http://arxiv.org/abs/0905.0556
Autor:
Littlestone-Luria, Adam
Publikováno v:
New York University Journal of Legislation & Public Policy; 2024, Vol. 26 Issue 2, p397-482, 86p
Publikováno v:
Multidiscipline Modeling in Materials and Structures, 2012, Vol. 8, Issue 3, pp. 297-331.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/15736101211269122
Publikováno v:
In Information and Computation 2000 161(2):85-139
Publikováno v:
In Information and Computation 2000 161(2):140-171
Autor:
A. Arakere, A. Littlestone, Roshdy George S. Barsoum, B. Pandurangan, A. Grujicic, Mica Grujicic
Publikováno v:
Multidiscipline Modeling in Materials and Structures. 8:297-331
PurposePolyurea falls into a category of elastomeric co‐polymers in which, due to the presence of strong hydrogen bonding, the microstructure is of a heterogeneous nature and consists of a compliant/soft matrix and stiff/hard nanometer size hard do
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::696fcd117c7b2061d82c90c269c7b164
https://doi.org/10.1108/15736101211269122
https://doi.org/10.1108/15736101211269122
Publikováno v:
Machine Learning. 43:173-210
The problem of learning linear-discriminant concepts can be solved by various mistake-driven update procedures, including the Winnow family of algorithms and the well-known Perceptron algorithm. In this paper we define the general class of “quasi-a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5761993cc7ab2376325ec6866dad41a9
https://doi.org/10.1023/a:1010844028087
https://doi.org/10.1023/a:1010844028087
Publikováno v:
Information and Computation. 161:85-139
In the standard on-line model the learning algorithm tries to minimizethe total number of mistakes made in a series of trials. On each trial the learner sees an instance, makes a prediction of its classification, then finds out the correct classifica
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::030c27f50e9e2ce628bb34c86a89cea8
https://doi.org/10.1006/inco.2000.2870
https://doi.org/10.1006/inco.2000.2870
Publikováno v:
STOC
We present an algorithm for the on-line learning of linear functions which is optimal to within a constant factor with respect to bounds on the sum of squared errors for a worst case sequence of trials. The bounds are logarithmic in the number of var
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ad35e14d7963abb38a3687ca71b7a312
https://doi.org/10.1007/bf01277953
https://doi.org/10.1007/bf01277953
Publikováno v:
Journal of Computer and System Sciences. 50(1):32-40
This paper addresses the problem of learning boolean functions in query and mistake-bound models in the presence of irrelevant attributes. In learning a concept, a learner may observe a great many more attributes than those that the concept depends u
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::903a1b2eb64c13d606044cd1eba75007