An exploration of improvements to semi-supervised fuzzy c-means clustering for real-world biomedical data
This thesis explores various detailed improvements to semi-supervised learning (using labelled data to guide clustering or classification of unlabelled data) with fuzzy c-means clustering (a ‘soft’ clustering technique which allows data patterns to be assigned to multiple clusters using membership v...
Disimpan dalam:
| Pengarang Utama: | |
|---|---|
| Format: | Thesis (University of Nottingham only) |
| Bahasa: | English |
| Diterbitkan: |
2014
|
| Capaian Atas Talian: | http://eprints.nottingham.ac.uk/14232/ http://eprints.nottingham.ac.uk/14232/1/correction_noblue.pdf |
| Penanda-penanda: |
Tambah Penanda
Tiada Penanda, Jadilah orang pertama menanda rekod ini!
|