TY - GEN
T1 - A neural network model for sequential multitask pattern recognition problems
AU - Nishikawa, Hitoshi
AU - Ozawa, Seiichi
AU - Roy, Asim
N1 - Funding Information:
The authors would like to thank Professor Shigeo Abe for his helpful comments and discussions. This research was partially supported by the Ministry of Education, Science, Sports and Culture, Grant-in-Aid for Scientific Research (C) 205002205.
PY - 2009
Y1 - 2009
N2 - In this paper, we propose a new multitask learning (MTL) model which can learn a series of multi-class pattern recognition prob- lems stably. The knowledge transfer in the proposed MTL model is implemented by the following mechanisms: (1) transfer by sharing the internal representation of RBFs and (2) transfer of the information on class subregions from the related tasks. The proposed model can detect task changes on its own based on the output errors even though no task information is given by the environment. It also learn training samples of different tasks that are given one after another. In the experiments, the recognition performance is evaluated for the eight MTPR problems which are defined from the four UCI data sets. The experimental results demonstrate that the proposed MTL model outperforms a single-task learning model in terms of the final classification accuracy. Furthermore, we show that the transfer of class subregion contributes to enhancing the generalization performance of a new task with less training samples.
AB - In this paper, we propose a new multitask learning (MTL) model which can learn a series of multi-class pattern recognition prob- lems stably. The knowledge transfer in the proposed MTL model is implemented by the following mechanisms: (1) transfer by sharing the internal representation of RBFs and (2) transfer of the information on class subregions from the related tasks. The proposed model can detect task changes on its own based on the output errors even though no task information is given by the environment. It also learn training samples of different tasks that are given one after another. In the experiments, the recognition performance is evaluated for the eight MTPR problems which are defined from the four UCI data sets. The experimental results demonstrate that the proposed MTL model outperforms a single-task learning model in terms of the final classification accuracy. Furthermore, we show that the transfer of class subregion contributes to enhancing the generalization performance of a new task with less training samples.
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U2 - 10.1007/978-3-642-02490-0_100
DO - 10.1007/978-3-642-02490-0_100
M3 - Conference contribution
AN - SCOPUS:70349135509
SN - 3642024890
SN - 9783642024894
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 821
EP - 828
BT - Advances in Neuro-Information Processing - 15th International Conference, ICONIP 2008, Revised Selected Papers
T2 - 15th International Conference on Neuro-Information Processing, ICONIP 2008
Y2 - 25 November 2008 through 28 November 2008
ER -