Nobuhito Manome
Papers
1
Total Citations
5
H-Index
1
About
Nobuhito Manome is a researcher whose work bridges adaptive machine learning and neural computation, with a particular focus on developing algorithms that thrive in dynamic environments. His most-cited paper, "A Multi-armed Bandit Algorithm Available in Stationary or Non-stationary Environments Using Self-organizing Maps" (2019), introduces a novel approach that leverages self-organizing maps to enable reinforcement learning agents to automatically adjust to both stable and shifting conditions—a critical capability for real-world applications like robotics and online recommendation systems. With 5 citations, this work demonstrates his contribution to making bandit algorithms more robust without requiring prior knowledge of environmental changes. Manome’s research is notable for its practical elegance: by integrating unsupervised learning with decision-making, he offers a solution that reduces computational overhead while maintaining high performance. His achievements reflect a commitment to solving fundamental challenges in machine learning, particularly in non-stationary settings where traditional methods often fail. For students and researchers exploring adaptive algorithms, Manome’s work provides a clear, innovative path forward.
Research Focus
Key Achievements
Top Papers
- 1