… the task of automatically learning a parameter function g for text classification. Given a set
of example text classification problems, we wish to “metalearn” a new learning algorithm (as …

… progress of transfer learning on … Transfer Learning approach. We hypothesize that
introducing dependencies between parent and child categories is possible using transfer learning. …

… We assume that an instance has Z features, and to cope with our learning framework in text
classification, we define that each feature corresponds to a word in a dictionary. The value of …

… This research work uses machine learning and transfer learning classification algorithms.
These models are applicable to many natural processing tasks and work efficiently on these …

… in [11, 12, 13], we present results obtained from using TL for the task of text classification. …
investigations on TL for Text Classification. We conducted transfer experiments on seven …

… or not transfer learning is effective for text classification tasks. … the two types of transfer learning
(three types if non-transfer is … Experimental results revealed that the full transfer method is …

… and reinforcement learning, there has been a lack of documented success stories of novel
… of transfer learning in other areas. In this invited article, I will argue that transfer learning is in …

… -task learning framework for multi-domain text classification … shared representation network
and text classification layer. Two … representation, and the text classification layer maps it into a …

… We begin with the text classification task in the few-shot transfer learning way and the results
are shown in Table 2 and Figure 3. For simplicity, we only list the comparative results for 2 …

… was applied in image classification and in this work in text classification, the proposed …
active and transfer learning can be integrated into models that use images and text as input. …