Initialization method and initialization system for hybrid modeling

Abstract

Embodiments of the present disclosure provide an initialization method and an initialization system for hybrid modeling, the initialization method includes: generating a plurality of initial points for hybrid modeling; estimating the first effective number of iterations based on model complexity as the selected The number of iterations of the hybrid modeling; the hybrid modeling is performed on the training data set from each of the initial points to generate a corresponding hybrid model; and the corresponding hybrid model with the best accuracy is selected. The initial point is used as the best initialization point.

Description

technical field [0001] Various embodiments of the present disclosure relate to the field of machine learning, and more particularly, to an initialization method and an initialization system for hybrid modeling. Background technique [0002] Mixture models are probabilistic models that represent the existence of subpopulations within a large population. In general, a mixture model fits a mixture distribution that represents the probability distribution of a large group of observations. As an example of a hybrid model, the Hierarchical Hybrid Expert (HME) model has been widely used in many enterprise-level machine learning applications, such as for forecasting electricity demand and sales levels, etc. HME is more flexible than ordinary linear models, and it retains the important advantages of using probability-based rules to partition the feature space and using piecewise local linear experts. [0003] A challenge in HME modeling is the need to learn a large number of parame...

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Problems solved by technology

Since the initialization process is random, it usually takes m

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