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Scaling Multinomial Logistic Regression via Hybrid Parallel..:

, In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining,

Raman, Parameswaran ; Srinivasan, Sriram ; Matsushima, Shin... - p. 1460-1470 , 2019

Link: https://dl.acm.org/doi/10.1145/3292500.3330837

RT 
T1 <cTitle>Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining</cTitle>: T1 <Title>Scaling Multinomial Logistic Regression via Hybrid Parallelism</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3330837&Exemplar=1&LAN=DE
				
A1 Raman, Parameswaran
A1 Srinivasan, Sriram
A1 Matsushima, Shin
A1 Zhang, Xinhua
A1 Yun, Hyokun
A1 Vishwanathan, S.V.N.
PB ACM
YR 2019
K1 large scale machine learning
K1 multinomial logistic regression
K1 stochastic optimization
K1 Mathematics of computing
K1 Mathematical analysis
K1 Mathematical optimization
K1 Continuous optimization
K1 Stochastic control and optimization
K1 Computing methodologies
K1 Parallel computing methodologies
K1 Parallel algorithms
K1 Massively parallel algorithms
K1 Distributed computing methodologies
K1 Machine learning
K1 Symbolic and algebraic manipulation
K1 Symbolic and algebraic algorithms
K1 Optimization algorithms
K1 Learning paradigms
K1 Supervised learning
K1 Mathematical software
K1 Solvers
K1 Information systems
K1 Information retrieval
K1 Retrieval tasks and goals
K1 Recommender systems
SP 1460
OP 1470
LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3292500.3330837
DO https://dl.acm.org/doi/10.1145/3292500.3330837
SF ELIB - SuUB Bremen

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