منابع مشابه
Source-Selection-Free Transfer Learning
Transfer learning addresses the problems that labeled training data are insufficient to produce a high-performance model. Typically, given a target learning task, most transfer learning approaches require to select one or more auxiliary tasks as sources by the designers. However, how to select the right source data to enable effective knowledge transfer automatically is still an unsolved proble...
متن کاملOn Handling Negative Transfer and Imbalanced Distributions in Multiple Source Transfer Learning
Transfer learning has benefited many real-world applications where labeled data are abundant in source domains but scarce in the target domain. As there are usually multiple relevant domains where knowledge can be transferred, multiple source transfer learning (MSTL) has recently attracted much attention. However, we are facing two major challenges when applying MSTL. First, without knowledge a...
متن کاملMulti-Transfer: Transfer Learning with Multiple Views and Multiple Sources
Transfer learning, which aims to help the learning task in a target domain by leveraging knowledge from auxiliary domains, has been demonstrated to be effective in different applications, e.g., text mining, sentiment analysis, etc. In addition, in many real-world applications, auxiliary data are described from multiple perspectives and usually carried by multiple sources. For example, to help c...
متن کاملSource Free Transfer Learning for Text Classification
Transfer learning uses relevant auxiliary data to help the learning task in a target domain where labeled data is usually insufficient to train an accurate model. Given appropriate auxiliary data, researchers have proposed many transfer learning models. How to find such auxiliary data, however, is of little research so far. In this paper, we focus on the problem of auxiliary data retrieval, and...
متن کاملFuzzy Refinement-based Transductive Transfer Learning
In traditional machine learning there is an assumption that the training and test data are drawn from the same distribution. This assumption may not be satisfied in many real world applications because training and test data may come from different time periods or domains. This paper proposed a novel algorithm known as Fuzzy Refinement (FR) to take this difference into account. The algorithm ut...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Fuzzy Systems
سال: 2020
ISSN: 1063-6706,1941-0034
DOI: 10.1109/tfuzz.2019.2952792