.png){kind=icon}
Return
Adaptive local hyperplane classification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2008.01.014.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, a novel classifier, called adaptive local hyperplane, is proposed for pattern classification. The experimental results on 11 real data sets demonstrate that the proposed classifier outperforms, on average, all the other seven benchmarking classifiers. In particular, it is the best classifier in 10 out of 11 data sets, and it is the close second best for just one data set. (c) 2008 Elsevier B.V. All rights reserved.
Keywords:
pattern classification
nearest neighbor
manifold
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.5
Papers:
2.5W
Citations:
6.5W
Organization
Cited Papers
ShareSaveUsing neural networks for prediction of the subcellular location of proteins.png)
NUCLEIC ACIDS RESEARCH
OAAI
NUCLEIC ACIDS RESEARCHIF13.1
OAAIShareSaveShareSaveShareSaveShareSave no more
no more