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Friday, June 29, 2018

Definition and Use of Transfer Learning

Was pointed out to me that transfer learning was important to leveraging intelligence.    I had heard of it, but sought out a closer look at definition and areas of useful research, here is a start.  Not mentioned in any of the usual Quant methods in business books, but even starting with smaller or restricted prototypes can be seen as transfer learning.

Understanding Transfer Learning ....

Transfer learning,   From Wikipedia, the free encyclopedia: 
Transfer learning or inductive transfer is a research problem in machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem.[1] For example, knowledge gained while learning to recognize cars could apply when trying to recognize trucks. This area of research bears some relation to the long history of psychological literature on transfer of learning, although formal ties between the two fields are limited.  .... " 

" ... The earliest cited work on transfer in machine learning is attributed to Lorien Pratt, who formulated the discriminability-based transfer (DBT) algorithm in 1993.[2] ... In 1997, the journal Machine Learning published a special issue devoted to transfer learning,[3] and by 1998, the field had advanced to include multi-task learning,[4] along with a more formal analysis of its theoretical foundations.[5] Learning to Learn,[6] edited by Pratt and Sebastian Thrun, is a 1998 review of the subject. .... Transfer learning has also been applied in cognitive science, with the journal Connection Science publishing a special issue on reuse of neural networks through transfer in 1996 ... " 

A Survey on Transfer Learning:

ACM article abstract.

A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data-labeling efforts. 

In recent years, transfer learning has emerged as a new learning framework to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression, and clustering problems. In this survey, we discuss the relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift. We also explore some potential future issues in transfer learning research ... " 

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