Add to ORKGIn the field of data mining, transfer learning is the method of transferring knowledge from one domain into another. Using reviews from prisjakt.se, a Swedish price comparison site, and hotels.com this work investigate how the similarities between domains affect the results of transfer learning when using recurrent neural networks. We test several different domains with different characteristics, e.g. size and lexical similarity. In this work only relatively similar domains were used, the same target function was sought and all reviews were in Swedish. Regardless, the results are conclusive; transfer learning is often beneficial, but is highly dependent on the features of the domains and how they compare with each other’s
Part 1: Keynote PresentationsInternational audienceIn machine learning and data mining, we often enc...
In this paper we deal with the problem of measuring the similarity between training and tests datase...
As the field of machine learning grows, so do the publicly available datasets. However, in the field...
In the field of data mining, transfer learning is the method of transferring knowledge from one doma...
Sentiment analysis is a field within machine learning that focus on determine the contextual polarit...
In networks of independent entities that face similar predictive tasks, transfer machine learning en...
Cross-domain sentiment classifiers aim to predict the polarity (i.e. sentiment orientation) of targe...
The lack of training data is a common problem in machine learning. One solution to thisproblem is to...
Transfer learning is a new machine learning and data mining framework that allows the training and t...
Word similarity and word relatedness are fundamental to natural language processing and more general...
Transfer learning has been found helpful at enhancing the target domain's learning process by tr...
The idea of developing machine learning systems or Artificial Intelligence agents that would learn f...
Language models can be applied to a diverse set of tasks with great results, but training a language...
This paper shows how a neural network can model the way people who have acquired knowledge of an art...
Transfer learning is one of the popular methods for solving the problem that the models built on the...
Part 1: Keynote PresentationsInternational audienceIn machine learning and data mining, we often enc...
In this paper we deal with the problem of measuring the similarity between training and tests datase...
As the field of machine learning grows, so do the publicly available datasets. However, in the field...
In the field of data mining, transfer learning is the method of transferring knowledge from one doma...
Sentiment analysis is a field within machine learning that focus on determine the contextual polarit...
In networks of independent entities that face similar predictive tasks, transfer machine learning en...
Cross-domain sentiment classifiers aim to predict the polarity (i.e. sentiment orientation) of targe...
The lack of training data is a common problem in machine learning. One solution to thisproblem is to...
Transfer learning is a new machine learning and data mining framework that allows the training and t...
Word similarity and word relatedness are fundamental to natural language processing and more general...
Transfer learning has been found helpful at enhancing the target domain's learning process by tr...
The idea of developing machine learning systems or Artificial Intelligence agents that would learn f...
Language models can be applied to a diverse set of tasks with great results, but training a language...
This paper shows how a neural network can model the way people who have acquired knowledge of an art...
Transfer learning is one of the popular methods for solving the problem that the models built on the...
Part 1: Keynote PresentationsInternational audienceIn machine learning and data mining, we often enc...
In this paper we deal with the problem of measuring the similarity between training and tests datase...
As the field of machine learning grows, so do the publicly available datasets. However, in the field...