A Survey on Transfer Learning

@article{Pan2010ASO,
  title={A Survey on Transfer Learning},
  author={Sinno Jialin Pan and Qiang Yang},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2010},
  volume={22},
  pages={1345-1359}
}
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… 

Figures and Tables from this paper

Knowledge Transfer Using Cost Sensitive Online Learning Classification
TLDR
A survey on cost sensitive on machine learning and various methods used is focus on online learning methods.
Knowledge Transfer Using Cost Sensitive Online Learning Classification
TLDR
A survey on cost sensitive on machine learning and various methods used is focus on online learning methods.
Transfer Learning: Survey and Classification
TLDR
This survey paper explains transfer learning along with its categorization and provides examples and perspective related to transfer learning.
A survey of transfer learning
TLDR
This survey paper formally defines transfer learning, presents information on current solutions, and reviews applications applied toTransfer learning, which can be applied to big data environments.
Feature-based transfer learning with real-world applications
TLDR
A novel dimensionality reduction framework for transfer learning is proposed, which tries to reduce the distance between different domains while preserve data properties as much as possible and is general for many transfer learning problems when domain knowledge is unavailable.
Feature Selection for Transfer Learning
TLDR
This paper presents a novel method to identify variant and invariant features between two datasets, and formalizes the problem of finding differently distributed features as a convex optimization problem.
Transfer Learning Techniques
TLDR
This survey paper formally defines transfer learning, presents information on current solutions, and reviews applications applied toTransfer learning, which can be applied to big data environments.
Transfer Learning with Ensemble of Multiple Feature Representations
  • Hang Zhao, Qing Liu, Yun Yang
  • Computer Science
    2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA)
  • 2018
TLDR
This paper proposes an instance-based transfer learning method, which is a weighted ensemble transfer learning framework with multiple feature representations, which achieves better performance than the traditional transferLearning method and the non-transfer learning method.
Semi-supervised Learning with Transfer Learning
TLDR
A novel transfer learning framework called TPTSVM (Transfer Progressive Transductive Support Vector Machine), which combines transfer learning and semi-supervised learning, which makes use of the limited labeled data in target domain to leverage a large amount of labeling data in source domain.
TLPCM: Transfer Learning Possibilistic $C$-Means
TLDR
A transfer-learning possibilistic c-means (TLPCM) algorithm is proposed to handle the PCM clustering problem in a domain that has insufficient data, and it overcomes the problem of differing numbers of clusters between the source and target domains.
...
...

References

SHOWING 1-10 OF 95 REFERENCES
Bridged Refinement for Transfer Learning
TLDR
A novel algorithm, namely bridged refinement, to take the shift of distribution into consideration is proposed, which corrects the labels predicted by a shift-unaware classifier towards a target distribution and takes the mixture distribution of the training and test data as a bridge to better transfer from the training data to the test data.
Dataset Shift in Machine Learning
TLDR
This volume offers an overview of current efforts to deal with dataset and covariate shift, and places dataset shift in relationship to transfer learning, transduction, local learning, active learning, and semi-supervised learning.
Transferring Naive Bayes Classifiers for Text Classification
TLDR
This paper proposes a novel transfer-learning algorithm for text classification based on an EM-based Naive Bayes classifiers and shows that the algorithm outperforms the traditional supervised and semi-supervised learning algorithms when the distributions of the training and test sets are increasingly different.
Transfer Learning via Dimensionality Reduction
TLDR
A new dimensionality reduction method is proposed to find a latent space, which minimizes the distance between distributions of the data in different domains in a latentspace, which can be treated as a bridge of transferring knowledge from the source domain to the target domain.
Spectral domain-transfer learning
TLDR
This paper formulate this domain-transfer learning problem under a novel spectral classification framework, where the objective function is introduced to seek consistency between the in-domain supervision and the out-of-domain intrinsic structure through optimization of the cost function.
Self-taught learning: transfer learning from unlabeled data
TLDR
An approach to self-taught learning that uses sparse coding to construct higher-level features using the unlabeled data to form a succinct input representation and significantly improve classification performance.
Analysis of Representations for Domain Adaptation
TLDR
The theory illustrates the tradeoffs inherent in designing a representation for domain adaptation and gives a new justification for a recently proposed model which explicitly minimizes the difference between the source and target domains, while at the same time maximizing the margin of the training set.
Logistic regression with an auxiliary data source
TLDR
This paper proposes a method to relax the requirement to draw examples from the same source distribution in the context of logistic regression, called "Migratory-Logit" or M- logit, which is demonstrated successfully on simulated as well as real data sets.
Improving SVM accuracy by training on auxiliary data sources
TLDR
Experiments show that when the training data set is very small, training with auxiliary data can produce large improvements in accuracy, even when the auxiliary data is significantly different from the training (and test) data.
Domain Adaptation for Statistical Classifiers
TLDR
This work introduces a statistical formulation of this problem in terms of a simple mixture model and presents an instantiation of this framework to maximum entropy classifiers and their linear chain counterparts and leads to improved performance on three real world tasks on four different data sets from the natural language processing domain.
...
...