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Dharuman, C.
- A Comparative Study of Principal Component Regression and Partial least Squares Regression with Application to FTIR Diabetes Data
Abstract Views :851 |
PDF Views:165
Authors
Affiliations
1 Department of Statistics, Tuberculosis Research Centre, ICMR, Chennai-600 031, IN
2 P. G. Department of Mathematics, Pachaiyappa’s College, Chennai-600 030, IN
3 P. G. Department of Physics, Pachaiyappa’s College, Chennai-600 030
1 Department of Statistics, Tuberculosis Research Centre, ICMR, Chennai-600 031, IN
2 P. G. Department of Mathematics, Pachaiyappa’s College, Chennai-600 030, IN
3 P. G. Department of Physics, Pachaiyappa’s College, Chennai-600 030
Source
Indian Journal of Science and Technology, Vol 4, No 7 (2011), Pagination: 740-746Abstract
In recent years, Fourier Transform Infrared (FT-IR) spectroscopy has had an increasingly important role in the field of pathology and diagnosis of disease states. The principal component regression (PCR) and the partial least squares regression (PLS) are the often proposed methods and widely used in FTIR data analysis, when the number of explanatory variable is relatively large in comparison to the samples as the least squares estimator may fail in such situations. They provide biased estimators with the relatively smaller variation than the variance of the least squares estimators. In this paper, a FTIR diabetes dataset is used in order to examine the performance of the two biased regression models on prediction. The conclusion is that for prediction PCR and PLS provides similar results which require substantial verification for any claims as to the superiority of any of the two biased regression methods.Keywords
Fourier Transform Infrared, Principal Component Regression, Partial least Square, Diabetes DataFull Text
References
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- Machine Learning Based Pattern Recognition for Chemical Spectral Data
Abstract Views :360 |
PDF Views:65
Authors
Affiliations
1 Head Dept. of Mathematics, Pachaiyappa's College, E.V.R.Periyar High Road, Shenoy Nagar, Chennai-600 030, IN
1 Head Dept. of Mathematics, Pachaiyappa's College, E.V.R.Periyar High Road, Shenoy Nagar, Chennai-600 030, IN
Source
Indian Journal of Innovations and Developments, Vol 1, No 6 (2012), Pagination: 412-430Abstract
The most common use for neural networks is to project what will most likely happen. There are many applications where prediction can help in setting priorities. Know who needs the most time critical help can enable a more successful operation. Basically, all organizations must establish priorities which govern the allocation of their resources. This projection of the future is what drove the creation of networks of prediction. In our study, we was examined the machine learning based pattern recognition for chemical spectral data.Keywords
Machine Learning, Pattern Recognition, Chemical-spectral Data, Intelligent Information System, Q-learning, Artificial Neural NetworkFull Text
References
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- Yegnanarayana (1994) Artificial neural networks for pattern recognition, 19(2), 189-238.
- Markov Chain Monte Carlo based Pattern Analysis for Diabetic Spectral Data
Abstract Views :243 |
PDF Views:0
Authors
C. Dharuman
1,
P. Venkatesan
2
Affiliations
1 SRM University, Ramapuram Campus, Chennai - 600089, Tamil Nadu, IN
2 Sri Ramachandra University, Porur, Chennai - 600116, Tamil Nadu, IN
1 SRM University, Ramapuram Campus, Chennai - 600089, Tamil Nadu, IN
2 Sri Ramachandra University, Porur, Chennai - 600116, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 8, No 33 (2015), Pagination:Abstract
Markov Chain Monte Carlo (MCMC) methods have been successfully used to overcome problems involving high dimensional diabetic spectral data. The objective is to show that the onset of a disease changes the relative content of the Bio-Molecules. The problem is formulated and solved in Bayesian frame work by using MCMC algorithm. The efficiency of the models are discussed, evaluated and compared.Keywords
Bayesian, Diabetic Spectral Data, Markov Chain Monte Carlo, Pattern AnalysisFull Text