Feasibility of Active Machine Learning for Multiclass Compound Classification.
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Large-Scale Off-Target Identification Using Fast and Accurate Dual Regularized One-Class Collaborative Filtering and Its Application to Drug RepurposingActive learning for computational chemogenomics.The value of prior knowledge in machine learning of complex network systems.Classifiers and their Metrics Quantified.
P2860
Feasibility of Active Machine Learning for Multiclass Compound Classification.
description
2016 nî lūn-bûn
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2016年の論文
@ja
2016年論文
@yue
2016年論文
@zh-hant
2016年論文
@zh-hk
2016年論文
@zh-mo
2016年論文
@zh-tw
2016年论文
@wuu
2016年论文
@zh
2016年论文
@zh-cn
name
Feasibility of Active Machine Learning for Multiclass Compound Classification.
@en
type
label
Feasibility of Active Machine Learning for Multiclass Compound Classification.
@en
prefLabel
Feasibility of Active Machine Learning for Multiclass Compound Classification.
@en
P50
P356
P1476
Feasibility of Active Machine Learning for Multiclass Compound Classification.
@en
P2093
Tobias Lang
P356
10.1021/ACS.JCIM.5B00332
P577
2016-01-07T00:00:00Z