Ensemble Models for Heterogeneous Data Sources For Supporting machine and deep learning

2019-2020 tavasz

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Téma leírása

Basing a decision on a single data model can lead to prediction impairment. A series of training courses in the field of education and training; In this project, we present a cross-exogenous multi-modeling technique for blending probabilities for predicting image data. Encouraging results were obtained by stacking the Restricted Boltzmann Machine with a multitask prediction layer. Promising results could be achieved by using a backward rolling window to calculate the auto-correlation to find an optimized window size. 

After examining and researching in this field, the following research is carried out:  And, in particular, the following is a list of the following:

 When used in ensemble Prediction, the utility of our software will help to improve the quality of the images. That's why, in the wake of this, we have to be a smart system.

https://www.youtube.com/watch?v=m-S9Hojj1as

https://www.youtube.com/watch?v=URdnFlZnlaE

Feltételek

  • gépi tanulás, machine learning, deep learning, GANs

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