In most traditional machine learning algorithms, the training and testing datasets have
identical distributions and feature spaces. However, these assumptions have not held in …

Transfer learning method has been widely used in machine learning when training data is
limited. However, class noise accumulated during learning iterations can lead to negative …

The integration of transfer learning methods and other machine learning branches can bring
a good improvement in speed and performance, it has become a good research topic in the …

Transfer learning (TL) has been successfully applied to many real-world problems that
traditional machine learning (ML) cannot handle, such as image processing, speech …

The cellular learning automaton (CLA), which is a combination of cellular automaton (CA)
and learning automaton (LA), is introduced recently. This model is superior to CA because of …

As a new classification platform, deep learning has recently received increasing attention
from researchers and has been successfully applied to many domains. In some domains …

Machine learning, as an important branch of artificial intelligence, is becoming increasingly
popular. Machine learning makes it possible to learn from massive training data and …

Transfer learning (TL) utilizes data or knowledge from one or more source domains to
facilitate learning in a target domain. It is particularly useful when the target domain has very …

Transfer learning enables solving a specific task having limited data by using the pre-trained
deep networks trained on large-scale datasets. Typically, while transferring the learned …

Machine learning is a rapidly growing field of computer science and has been growing
exponentially within the last few years. This is because of massive growth of dataset being …