A Multitask Learning Model for Online Pattern Recognition
| Autor: | Seiichi Ozawa, Dmitri Roussinov, Asim Roy |
|---|---|
| Rok vydání: | 2009 |
| Předmět: |
Computer Networks and Communications
Process (engineering) Computer science business.industry Stability (learning theory) Multi-task learning Pattern recognition General Medicine Machine learning computer.software_genre Knowledge acquisition Computer Science Applications Task (project management) Multiclass classification Categorization Artificial Intelligence Pattern recognition (psychology) Artificial intelligence business computer Software |
| Zdroj: | IEEE Transactions on Neural Networks. 20:430-445 |
| ISSN: | 1941-0093 1045-9227 |
| DOI: | 10.1109/tnn.2008.2007961 |
| Popis: | This paper presents a new learning algorithm for multitask pattern recognition (MTPR) problems. We consider learning multiple multiclass classification tasks online where no information is ever provided about the task category of a training example. The algorithm thus needs an automated task recognition capability to properly learn the different classification tasks. The learning mode is ldquoonlinerdquo where training examples for different tasks are mixed in a random fashion and given sequentially one after another. We assume that the classification tasks are related to each other and that both the tasks and their training examples appear in random during ldquoonline training.rdquo Thus, the learning algorithm has to continually switch from learning one task to another whenever the training examples change to a different task. This also implies that the learning algorithm has to detect task changes automatically and utilize knowledge of previous tasks for learning new tasks fast. The performance of the algorithm is evaluated for ten MTPR problems using five University of California at Irvine (UCI) data sets. The experiments verify that the proposed algorithm can indeed acquire and accumulate task knowledge and that the transfer of knowledge from tasks already learned enhances the speed of knowledge acquisition on new tasks and the final classification accuracy. In addition, the task categorization accuracy is greatly improved for all MTPR problems by introducing the reorganization process even if the presentation order of class training examples is fairly biased. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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