Tetsuya Yamane
Papers
1
Total Citations
9
H-Index
1
About
Tetsuya Yamane is a researcher whose work lies at the intersection of human-machine interaction and intelligent pattern recognition. His primary research focuses on developing systems that enable robots to learn and communicate more naturally with humans, particularly through voice commands. Yamane's most notable contribution is the Parameter-less Growing Self-Organizing Map (PL-G-SOM), an innovative pattern recognition model that eliminates the need for manual parameter tuning. This breakthrough is central to his 2011 paper "A Human-Machine Interaction System: A Voice Command Learning System Using PL-G-SOM," which has garnered 9 citations and demonstrates how robots can acquire communication abilities from instructors. The work also explores the concept of computational "feeling" in robots, adding an emotional dimension to machine learning. While his citation count is modest, Yamane's contributions to self-organizing maps and human-robot interaction represent important steps toward more intuitive and adaptive robotic systems, making his research valuable for students and researchers interested in the future of autonomous learning machines.
Research Focus
Key Achievements
Top Papers
- 1