Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Murad Tukan"'
Publikováno v:
Sensors, Vol 21, Iss 19, p 6689 (2021)
Coreset is usually a small weighted subset of an input set of items, that provably approximates their loss function for a given set of queries (models, classifiers, hypothesis). That is, the maximum (worst-case) error over all queries is bounded. To
Externí odkaz:
https://doaj.org/article/26bcef9252b0428280e372df224f3a98
Publikováno v:
Sensors, Vol 21, Iss 16, p 5599 (2021)
A common technique for compressing a neural network is to compute the k-rank ℓ2 approximation Ak of the matrix A∈Rn×d via SVD that corresponds to a fully connected layer (or embedding layer). Here, d is the number of input neurons in the layer,
Externí odkaz:
https://doaj.org/article/18b56702a14f476393df979512e7125a
Publikováno v:
Theoretical Computer Science. 890:171-191
We present an efficient coreset construction algorithm for large-scale Support Vector Machine (SVM) training in Big Data and streaming applications. A coreset is a small, representative subset of the original data points such that a model trained on
Publikováno v:
Sensors
Volume 21
Issue 16
Sensors (Basel, Switzerland)
Sensors, Vol 21, Iss 5599, p 5599 (2021)
Volume 21
Issue 16
Sensors (Basel, Switzerland)
Sensors, Vol 21, Iss 5599, p 5599 (2021)
A common technique for compressing a neural network is to compute the k-rank ℓ2 approximation Ak of the matrix A∈Rn×d via SVD that corresponds to a fully connected layer (or embedding layer). Here, d is the number of input neurons in the layer,
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030592660
TAMC
TAMC
We present an efficient coreset construction algorithm for large-scale Support Vector Machine (SVM) training in Big Data and streaming applications. A coreset is a small, representative subset of the original data points such that a models trained on
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dc75541fa2fc4249bc927d999e0e6341
https://doi.org/10.1007/978-3-030-59267-7_25
https://doi.org/10.1007/978-3-030-59267-7_25