Using particle swarms for the development of QSAR models based on K-nearest neighbor and kernel regression.
about
Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network trainingAdvances in computational methods to predict the biological activity of compounds.Machine Learning on Signal-to-Noise Ratios Improves Peptide Array Design in SAMDI Mass SpectrometryDefining a novel k-nearest neighbours approach to assess the applicability domain of a QSAR model for reliable predictions.QSAR MODEL OF THE QUORUM-QUENCHING N-ACYL-HOMOSERINE LACTONE LACTONASE ACTIVITY
P2860
Using particle swarms for the development of QSAR models based on K-nearest neighbor and kernel regression.
description
2003 nî lūn-bûn
@nan
2003年の論文
@ja
2003年学术文章
@wuu
2003年学术文章
@zh-cn
2003年学术文章
@zh-hans
2003年学术文章
@zh-my
2003年学术文章
@zh-sg
2003年學術文章
@yue
2003年學術文章
@zh
2003年學術文章
@zh-hant
name
Using particle swarms for the ...... eighbor and kernel regression.
@en
Using particle swarms for the ...... eighbor and kernel regression.
@nl
type
label
Using particle swarms for the ...... eighbor and kernel regression.
@en
Using particle swarms for the ...... eighbor and kernel regression.
@nl
prefLabel
Using particle swarms for the ...... eighbor and kernel regression.
@en
Using particle swarms for the ...... eighbor and kernel regression.
@nl
P356
P1476
Using particle swarms for the ...... eighbor and kernel regression.
@en
P2093
Dimitris K Agrafiotis
Walter Cedeño
P2888
P304
P356
10.1023/A:1025338411016
P577
2003-02-01T00:00:00Z
P5875
P6179
1048930704