… Transfer learning focuses on extracting knowledge from a source domain, then … seeded-transfer
learning approach to address the road roughness modeling and prediction problem. We …

… on the application of transfer learning to regression tasks. Transfer learning has achieved …
In deep neural networks, the shallow layers are responsible for general feature extraction, …

… Seeded transfer learning is also used for regression problem [36]. The approach used was
different from the existing literature in a way that it uses few data in target domain like seeds …

… area of transfer learning in GP for symbolic regression is still … The strategy is to reuse part of
a deep neural network, usually … points for learning, and present their own seeding method to …

… to layered learning and population seeding [8]. … , machine learning, classification, symbolic
regression, feature selection, evolving deep neural networks, image analysis, transfer learning…

… [54] present a seeded sampling technique for transfer learning that we extend to form the …
In the following section, we describe how we utilize the concept used by seeded sampling for …

… Google AI team proposes to train a deep neural network to … detection task as a regression
or classification problem. A single … As such, the improved seeding method ensures a smarter …

… Providing such expensive resources for machine diagnosis is highly challenging. Transfer
learning recently emerged as a valuable approach to address these issues. Thus, the …

… Due to the success of the seeding algorithm on improving the initial GPSR population …
seeding approaches for the assumption 2 dataset. Here, the initial GPSR population is seeded …

… or regression. Softmax … machine learning methods, pure support vector machine (SVM)
and k-nearest neighbor (KNN), and two deep learning algorithms, deep neural network (DNN) …