Price: $81.00 – $69.69
(as of Dec 27,2024 01:37:31 UTC – Details)
Publisher : Mdpi AG (August 17, 2021)
Language : English
Hardcover : 316 pages
ISBN-10 : 3036512888
ISBN-13 : 978-3036512884
Item Weight : 1.94 pounds
Dimensions : 6.69 x 1 x 9.61 inches
Machine learning is a powerful tool that has revolutionized many industries, but it often requires large, clean datasets to produce accurate results. However, in real-world scenarios, datasets are often noisy, incomplete, or small. In these cases, traditional machine learning methods may not be effective.
There are several techniques that can be used to work with noisy, incomplete, or small datasets in machine learning. One approach is to use data augmentation, where synthetic data is generated to supplement the original dataset. This can help to increase the size of the dataset and improve the model’s performance.
Another technique is to use transfer learning, where a pre-trained model is used as a starting point and fine-tuned on the smaller, noisier dataset. This can help to leverage the knowledge learned from the larger, cleaner dataset and adapt it to the new data.
Ensemble methods, which combine multiple models to make predictions, can also be effective in dealing with noisy or incomplete datasets. By averaging the predictions of multiple models, ensemble methods can reduce the impact of noise and improve the overall accuracy of the model.
Overall, while working with noisy, incomplete, or small datasets can be challenging, there are a variety of machine learning methods that can help to overcome these obstacles and produce accurate results. By leveraging techniques such as data augmentation, transfer learning, and ensemble methods, it is possible to build effective machine learning models even with imperfect data.
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