Nianyi Chen

Support vector machine in chemistry

In recent years, the support vector machine (SVM), a new data processing method, has been applied to many fields of chemistry and chemical technology. Compared with some other data processing methods, SVM is especially suitable for solving problems of small sample size, with superior prediction performance. SVM is fast becoming a powerful tool of chemometrics. This book provides a systematic approach to the principles and algorithms of SVM, and demonstrates the application examples of SVM in QSAR/QSPR work, materials and experimental design, phase diagram prediction, modeling for the optimal control of chemical industry, and other branches in chemistry and chemical technology.

Semiempirical Quantum Chemical Method and Artificial Neural Networks Applied for λmax Computation of Some Azo Dyes

The maximum absorption wavelengths of 31 azo dyes have been calculated by two comprehensive methods using the semiempirical quantum chemical method, PM3, and the weight decay based artificial neural network (WD-ANN) or the early stopping based artificial neural network (ES-ANN). The average absolute errors of WD-ANN and that of ES-ANN are 10.07 nm and 12.40 nm, respectively. These results are much better than the results using ZINDO/S with the default value (0.585) only.

On Multivariate Calibration Problems

Multivariate calibration is a classic problem in the analytical chemistry field and frequently solved by partial least squares method in the previous work. Unfortunately there are so many redundant features in the problem, that feature selection are often performed before modeling by partial least squares method and the features not selected are usually discarded. In this paper, the redundant information is, however, reused in the learning of partial least squares method within the frame of multitask learning. Results on three multivariate calibration data sets show that multitask learning can greatly improve the accuracy of partial least squares method.