Performance evaluation of relevance vector machines as a nonlinear regression method in real-world chemical spectroscopic data

Porro, Diana; Hdez, N.; Talavera, Isneri; Núñez, Óscar; Dago, Angel; Biscay, Rolando J.

doi:10.1109/icpr.2008.4761236

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…Tipping [30] illustrated the RVM's predictive ability on some popular benchmarks by comparing it with the SVM. The empirical analysis also proved that the RVM outperformed SVM; some other applications of RVM in prediction problems are referred to [31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Recurrent Support and Relevance Vector Machines Based Model with Application to Forecasting Volatility of Financial Returns

Hossain¹,

Nasser²

2011

JILSA

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In the recent years, the use of GARCH type (especially, ARMA-GARCH) models and computational-intelligence-based techniques—Support Vector Machine (SVM) and Relevance Vector Machine (RVM) have been successfully used for financial forecasting. This paper deals with the application of ARMA-GARCH, recurrent SVM (RSVM) and recurrent RVM (RRVM) in volatility forecasting. Based on RSVM and RRVM, two GARCH methods are used and are compared with parametric GARCHs (Pure and ARMA-GARCH) in terms of their ability to forecast multi-periodically. These models are evaluated on four performance metrics: MSE, MAE, DS, and linear regression R squared. The real data in this study uses two Asian stock market composite indices of BSE SENSEX and NIKKEI225. This paper also examines the effects of outliers on modeling and forecasting volatility. Our experiment shows that both the RSVM and RRVM perform almost equally, but better than the GARCH type models in forecasting. The ARMA-GARCH model is superior to the pure GARCH and only the RRVM with RSVM hold the robustness properties in forecasting

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Section: Introductionmentioning

confidence: 99%

Recurrent Support and Relevance Vector Machines Based Model with Application to Forecasting Volatility of Financial Returns

Hossain¹,

Nasser²

2011

JILSA

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One‐dimensional convolutional neural networks for spectroscopic signal regression

Malek

Melgani

Bazi

2017

Journal of Chemometrics

192

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This paper proposes a novel approach for driving chemometric analyses from spectroscopic data and based on a convolutional neural network (CNN) architecture. For such purpose, the well‐known 2‐D CNN is adapted to the monodimensional nature of spectroscopic data. In particular, filtering and pooling operations as well as equations for training are revisited. We also propose an alternative to train the resulting 1D‐CNN by means of particle swarm optimization. The resulting trained CNN architecture is successively exploited to extract features from a given 1D spectral signature to feed any regression method. In this work, we resorted to 2 advanced and effective methods, which are support vector machine regression and Gaussian process regression. Experimental results conducted on 3 real spectroscopic datasets show the interesting capabilities of the proposed 1D‐CNN methods.

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Incremental relevance sample-feature machine: A fast marginal likelihood maximization approach for joint feature selection and classification

Mohsenzadeh

Sheikhzadeh

Nazari

2016

Pattern Recognition

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Performance evaluation of relevance vector machines as a nonlinear regression method in real-world chemical spectroscopic data

Cited by 4 publications

References 7 publications

Recurrent Support and Relevance Vector Machines Based Model with Application to Forecasting Volatility of Financial Returns

Recurrent Support and Relevance Vector Machines Based Model with Application to Forecasting Volatility of Financial Returns

One‐dimensional convolutional neural networks for spectroscopic signal regression

Incremental relevance sample-feature machine: A fast marginal likelihood maximization approach for joint feature selection and classification

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