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Distributed training of Large-scale Logistic models
International Conference on Machine Learning
Regularized Multinomial Logistic regression has emerged as one of the most common methods for performing data classification and analysis. With the advent of large-scale data it is common to find scenarios where the number of possible multinomial outcomes is large (in the order of thousands to tens of thousands) and the dimensionality is high. In such cases, the computational cost of training logistic models or even simply iterating through all the model parameters is prohibitively expensive.dblp:conf/icml/GopalY13 fatcat:z5zdn57agjga7p35vhi7dasoby