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We refer to the problem as a -regularized logistic regression problem (l1-regularized LRP).
Linear regression problem is a widely used problem in many metrological and measurement systems.
First-order methods are the algorithms of choice for solving large-scale instances of the logistic regression problem.
And you can prove that this simple problem allow you, for example, to describe many vector [INAUDIBLE] regression problem.
Fractional-order extreme learning machine is presented for a regression problem in this paper.
In this study, we address the regression problem on set-valued samples that appear in applications.
The algorithm solves a block isotonic regression problem in the projection step in linear time.
This paper presents the novel hierarchical deep neural network (HDNN) for the general multivariate regression problem.
The discrete-time variant of this task is commonly reformulated as a regression problem.
We consider the nonparametric regression problem, where we take fixed design points xi∈[0,1].
Regression problem is an important application area for neural networks (NNs).
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