Abstract
This paper proposes an Extended Minimum Description Length Principle (EMDLP) for feature-based inductive transfer learning, in which both the source and the target data sets contain class labels and relevant features are transferred from the source domain to the target one. Despite numerous works on this topic, few of them have a solid theoretical framework and are parameter-free. Our EMDLP overcomes these flaws and allows us to evaluate the inferiority of the results of transfer learning with the add-sum of the code lengths of five components: the corresponding two hypotheses, the two data sets with the help of the hypotheses, and the set of the transferred features. We design a code book to build the connections between the source and the target tasks. Extensive experiments using both real and artificial data sets show that EMDLP is robust against noise and performs better on the classification accuracy than the state-of-the-art methods.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011 |
| Pages | 259-270 |
| Number of pages | 12 |
| Publication status | Published - 2011 |
| Event | 11th SIAM International Conference on Data Mining, SDM 2011 - Mesa, AZ, United States Duration: Apr 28 2011 → Apr 30 2011 |
Other
| Other | 11th SIAM International Conference on Data Mining, SDM 2011 |
|---|---|
| Country | United States |
| City | Mesa, AZ |
| Period | 4/28/11 → 4/30/11 |
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All Science Journal Classification (ASJC) codes
- Software
Cite this
Feature-based inductive transfer learning through minimum encoding. / Shao, Hao; Suzuki, Einoshin.
Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011. 2011. p. 259-270.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Feature-based inductive transfer learning through minimum encoding
AU - Shao, Hao
AU - Suzuki, Einoshin
PY - 2011
Y1 - 2011
N2 - This paper proposes an Extended Minimum Description Length Principle (EMDLP) for feature-based inductive transfer learning, in which both the source and the target data sets contain class labels and relevant features are transferred from the source domain to the target one. Despite numerous works on this topic, few of them have a solid theoretical framework and are parameter-free. Our EMDLP overcomes these flaws and allows us to evaluate the inferiority of the results of transfer learning with the add-sum of the code lengths of five components: the corresponding two hypotheses, the two data sets with the help of the hypotheses, and the set of the transferred features. We design a code book to build the connections between the source and the target tasks. Extensive experiments using both real and artificial data sets show that EMDLP is robust against noise and performs better on the classification accuracy than the state-of-the-art methods.
AB - This paper proposes an Extended Minimum Description Length Principle (EMDLP) for feature-based inductive transfer learning, in which both the source and the target data sets contain class labels and relevant features are transferred from the source domain to the target one. Despite numerous works on this topic, few of them have a solid theoretical framework and are parameter-free. Our EMDLP overcomes these flaws and allows us to evaluate the inferiority of the results of transfer learning with the add-sum of the code lengths of five components: the corresponding two hypotheses, the two data sets with the help of the hypotheses, and the set of the transferred features. We design a code book to build the connections between the source and the target tasks. Extensive experiments using both real and artificial data sets show that EMDLP is robust against noise and performs better on the classification accuracy than the state-of-the-art methods.
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M3 - Conference contribution
SN - 9780898719925
SP - 259
EP - 270
BT - Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011
ER -