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The influence of parameter initialization on the training time and accuracy of a nonlinear regression model

机译：参数初始化对非线性回归模型的训练时间和精度的影响

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摘要

In order to construct a nonlinear regression model we have to accurately (in some sense) initialize parameters of the model. In this work we performed comparison of several widely used methods and several newly developed approached for initialization of parameters of a regression model, represented as a decomposition in a linear dictionary of some parametric functions (sigmoids). We proposed a general deterministic approach for initialization, providing repeatability of results, reduction of a learning time and in some cases increase of a regression model accuracy; we developed two new algorithms (based on a piecewise-linear approximation and based on local properties of approximable dependency) in the framework of the proposed approach; we developed randomized initialization algorithm (spherical initialization) for effective approximation of high-dimensional dependencies; we improved the classical initialization method SCAWI (by locating centers of sigmoids in sample points), providing a regression model accuracy improvement on specific classes of dependencies (smooth functions and discontinuous functions with a number of local peculiarities in an input domain) when using RProp algorithm for learning; we performed comparison of classical and newly proposed initialization methods and highlighted the most efficient ones.
机译：为了构建非线性回归模型，我们必须准确地（在某种意义上）初始化模型的参数。在这项工作中，我们比较了几种广泛使用的方法和几种新近开发的用于初始化回归模型参数的方法，这些参数表示为某些参数函数（S形）的线性字典中的分解。我们提出了一种用于初始化的通用确定性方法，该方法可提供结果的可重复性，减少学习时间，并在某些情况下提高回归模型的准确性。在提出的方法框架内，我们开发了两种新算法（基于分段线性逼近和基于近似依赖的局部属性）；我们开发了随机初始化算法（球形初始化）以有效逼近高维依存关系；我们改进了经典的初始化方法SCAWI（通过在样本点中定位S型中心），在使用RProp算法时，对特定类别的依存关系（在输入域中具有多个局部特性的平滑函数和不连续函数）提供了回归模型准确性的改进学习；我们比较了经典和新提出的初始化方法，并重点介绍了最有效的初始化方法。

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