Use of Molecular Similarity Indices for QSAR Training Set Selection

1995 ◽  
Vol 3 (4) ◽  
pp. 279-292 ◽  
Author(s):  
I. T. Cousins ◽  
M. T. D. Cronin ◽  
J. C. Dearden ◽  
C. D. Watts
Keyword(s):  
Molecular Similarity ◽  
Training Set ◽  
Similarity Indices ◽  
Training Set Selection

New Entropy Based Distance for Training Set Selection in Debt Portfolio Valuation

2012 ◽  
Vol 7 (2) ◽  
pp. 60-69
Author(s):  
Tomasz Kajdanowicz ◽  
Slawomir Plamowski ◽  
Przemyslaw Kazienko
Keyword(s):  
Machine Learning ◽  
Distance Measure ◽  
Prediction Performance ◽  
Training Set ◽  
Learning Tasks ◽  
Real Domain ◽  
Valuation Process ◽  
Proper Training ◽  
Training Set Selection ◽  
Portfolio Valuation

Choosing a proper training set for machine learning tasks is of great importance in complex domain problems. In the paper a new distance measure for training set selection is presented and thoroughly discussed. The distance between two datasets is computed using variance of entropy in groups obtained after clustering. The approach is validated using real domain datasets from debt portfolio valuation process. Eventually, prediction performance is examined.

scholarly journals Optimized spectral reconstruction based on adaptive training set selection

2017 ◽  
Vol 25 (11) ◽  
pp. 12435 ◽  
Author(s):  
Zhen Liu ◽  
Qiang Liu ◽  
Gui-ai Gao ◽  
Chan Li
Keyword(s):  
Training Set ◽  
Adaptive Training ◽  
Spectral Reconstruction ◽  
Training Set Selection
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