Airborne laser scanning is nowadays widely used for the estimation of forest stand parameters. Prediction models have to deal with high dimensional laser data sets as well as limited field calibration data. This problem is enhanced in mountainous areas where forest is highly heterogeneous and field data collection costly. Artificial neural network models and support vector regression (SVR) have...

In this paper, a new model, core vector regression (CVR) optimized by memetic algorithm (MA), is presented to predict electric daily load. Support vector regression (SVR) has obtained wide focus in recent years to solve nonlinear regression problems in many fields. However, it is limited on large scale dataset problem because of its high time and space complexity. Hence, CVR is proposed to impr...

The robust regression is an important tool for the analysis of data contamined by outliers. In computer vision, the optic flow computation is considered as belonging to this kind of problem. In this paper, we discuss a robust optic flow computation based on a modified support vector regression (SVR) technique. We experimentally show that the proposed method significantly improves the robustness...

This paper proposes a model for learning soft-monotonic regression functions in the presence of imperfect domain knowledge. It proposes an extension to support vector regression (SVR) wherein a new hardness parameter is introduced to configure the degree of monotonicity. The model supports multiple monotonicity constraints over multiple input dimensions simultaneously. The proposed model has be...

The objective of this study is to investigate the efficient determination of C and γ for Support Vector Regression with RBF or mahalanobis kernel based on numerical and statistician considerations, which indicates the connection between C and kernels and demonstrates that the deviation of geometric distance of neighbour observation in mapped space effects the predict accuracy of -SVR. We determ...

In the process industry on-line spectroscopic methods, like Raman and Near Infrared (NIR), in combination with regression tools are increasingly used to measure quality characteristics of products, e.g. concentrations of chemical constituents. One of the most widely used regression techniques is Partial Least Squares (PLS) and a relatively new, not yet widely used, technique is Support Vector R...

The objective of this study is to develop a method based on multivariate relevance vector regression (MVRVR) as a kernel-based Bayesian model for the estimation of above-ground biomass (AGB) in the Hyrcanian forests of Iran. Field AGB data from the Hyrcanian forests and multi-temporal PALSAR backscatter values are used for training and testing the methods. The results of the MVRVR method are th...

Remote sensing image analysis can be carried out at the per-pixel (hard) and sub-pixel (soft) scales. The former refers to the purity of image pixels, while the latter refers to the mixed spectra resulting from all objects composing of the image pixels. The spectral unmixing methods have been developed to decompose mixed spectra. Data-driven unmixing algorithms utilize the reference data called...

With the evidence framework, the regularized linear regression model can be explained as the corresponding MAP problem in this paper, and the general dependency relationships that the optimal parameters in this model with noisy input should follow is then derived. The support vector regression machines Huber-SVR and Norm-r r-SVR are two typical examples of thismodel and their optimal parameter ...

In this paper, a new econometric model of volatility is proposed using hybrid Support Vector machine for Regression (SVR) combined with Chaotic Genetic Algorithm (CGA) to fit conditional mean and then conditional variance of stock market returns. The CGA, integrated by chaotic optimization algorithm with Genetic Algorithm (GA), is used to overcome premature local optimum in determining three hy...