상세정보
학술대회
Weak False Label Learning Model for Sensor Data Recognition
- 발행일
- 201510
- 출처
- International Conference on Control, Automation and Systems (ICCAS) 2015, pp.1321-1323
- 협약과제
- 15MS5700, 후각 바이오 정보 기반 감성증강 인터랙티브 콘텐츠 기술 개발, 이해룡
- 초록
- Real world behavior recognitions tend to suffer from incomplete data because sensors are not perfect. Although machine learning algorithms are successfully applied to recognitions, they do not work well in multi-valued output functions because true and false label in same input collide in learning process. In this paper, we propose a noble algorithm which lessens multi-valued function's problem by weakening false labels. It also includes virtual samples and output normalization to compensate for the balance between true and false labels.
- KSP 제안 키워드
- Data recognition, Learning model, Machine Learning Algorithms, Multi-valued function, Real-world, incomplete data, learning process, sensor data, virtual samples