Abstract
This paper presents a complete approach to a successful utilization of a high-performance extreme learning machines (ELMs) Toolbox for Big Data. It summarizes recent advantages in algorithmic performance; gives a fresh view on the ELM solution in relation to the traditional linear algebraic performance; and reaps the latest software and hardware performance achievements. The results are applicable to a wide range of machine learning problems and thus provide a solid ground for tackling numerous Big Data challenges. The included toolbox is targeted at enabling the full potential of ELMs to the widest range of users.
| Original language | Undefined/Unknown |
|---|---|
| Peer-reviewed scientific journal | IEEE Access |
| Volume | 3 |
| Pages (from-to) | 1011-1025 |
| Number of pages | 15 |
| DOIs | |
| Publication status | Published - 2015 |
| MoE publication type | A1 Journal article - refereed |
Keywords
- 512 Business and Management
- Learning systems
- Performance evaluation
- Machine learninf