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Internet Electronic Journal of Molecular Design - IEJMD, ISSN 1538-6414, CODEN IEJMAT
| ABSTRACT - Internet Electron. J. Mol. Des. June 2005, Volume 4, Number 6, 381-392 |
Modeling Structure Property Relationships with Kernel Recursive Least Squares
Rajshekhar, Abhijit Kulkarni, Valadi K. Jayaraman, and Bhaskar D. Kulkarni
Internet Electron. J. Mol. Des. 2005, 4, 381-392
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Abstract:
Modeling structure property relationships accurately is a
challenging task and newly developed kernel based methods may
provide the accuracy for building these relationships. Kernelized
variant of traditional recursive least squares algorithm is used to
model two QSPR datasets. All the datasets showed a good
correlation between actual and predicted values of boiling points
with root mean squared errors (RMSEs) comparable to other
conventional methods. For the datasets from Espinosa et al., KRLS
showed good prediction statistics with R value in the range of
0.97-0.99 and S value in the range 5.5-8 as compared to multiple
linear regression (MLR) with R value in the range 0.85-0.88 and S
value in the range 22-26. For the dataset from Trinajstic et al.,
KRLS performed consistently well with R values lying in the range
of 0.95-0.99 and S in the range of 5-10 as compared to MLR with
R values in the range of 0.7-0.85 and S in the range of 25-30. The
KRLS method works better when more number of variables from
the dataset are included as against other methods such as support
vector learning or lazy learning technique which works better for
smaller number of reduced relevant variables from the dataset.
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