Prediction method and device based on multi-modal extreme learning machine, equipment and medium

Abstract

The invention discloses a prediction method and device based on a multi-mode extreme learning machine, equipment and a medium, and the method comprises the steps: obtaining training data and neuron weight parameters under probability distribution, and constructing a tag vector corresponding to the training data; constructing a plurality of intermediate neurons under each probability distribution according to the weight parameters of the neurons; according to the plurality of interneurons, constructing composite features of the training data under each probability distribution; second-order sample features corresponding to the composite features are calculated, and a kernel matrix corresponding to the second-order sample features is constructed; and obtaining a to-be-predicted sample, and predicting the to-be-predicted sample according to an extreme learning machine model jointly constructed by the kernel matrix and the label vector to obtain a prediction result. The method can be applied to solving data-driven modeling problems, such as image classification, sequence prediction, geophysics, credit evaluation and the like. The technical problem of unstable precision caused by random mapping of an extreme learning machine model in the prior art is solved.

Description

technical field [0001] This application relates to the field of big data technology, and in particular to a prediction method, device, equipment and medium based on a multimodal extreme learning machine. Background technique [0002] With the continuous development of machine learning, a variety of machine learning methods have emerged, among which the extreme learning machine model can be used to solve classification and regression problems, but the random mapping of the extreme learning machine model has always been a difficult problem, and also That is, during the training process, the hidden layers obtained by sampling different probability distributions are different, which leads to different prediction performances. In the training process of the current extreme learning machine model, the probability distribution is usually hand-selected by experience, which has great uncertainty. Therefore, it will affect the stability of the accuracy of the extreme learning machine ...

AI Technical Summary

Problems solved by technology

[0004] The main purpose of this application is to provide a prediction method, device, equipment and medium based on a multimodal extreme learning machine, aiming to solve the technical problem of unstable precision caused by random mapping in the existing extreme learning machine model

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