The elements of statistical learning. 2001

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Support Vector Machines

In a small case study of mixed hardwood Hyrcanian forests of Iran, three non-parametric methods, namely k -nearest neighbour k -NN, support vector machine regression SVR and tree regression based on random forest RF, were used in plot-level estimation of volume/ha, basal area/ha and stems/ha using field inventory and Advanced Spaceborne Thermal Emission and Reflection Radiometer ASTER data Relevant pre-processing and processing steps were applied to the ASTER data for geometric and atmospheric correction and for enhancing quantitative forest parameters After collecting terrestrial information on trees in the 101 samples, the volume, basal area and tree number per hectare were calculated in each plot In the k -NN implementation using different distance measures and k , the cross-validation method was used to find the best distance measure and optimal k  In SVR, the best regularized parameters of four kernel types were obtained using leave-one-out cross-validation RF was implemented using a bootstrap learning method with regularized parameters for decision tree model and stopping The validity of performances was examined using unused test samples by absolute and relative root mean square error RMSE and bias metrics In volume/ha estimation, the results showed that all the three algorithms had similar performances However, SVR and RF produced better results than k -NN with relative RMSE values of 2854, 2586 and 2686 m3 ha–1, respectively, using k -NN, SVR and RF algorithms, but RF could generate unbiased estimation In basal area/ha and stems/ha estimation, the implementation results of RF showed that RF was slightly superior in relative RMSE 1839, 2064 to SVR 1935, 2209 and k -NN 2020, 2153, but k -NN could generate unbiased estimation compared with the other two algorithms used

Forest attribute imputation using machine-learning methods and ASTER data: comparison of k-NN, SVR and random forest regression algorithms

Well, someone can decide by themselves what they want to do and need to do but sometimes, that kind of person will need some machine learning for audio image and video analysis references. People with open minded will always try to seek for the new things and information from many sources. On the contrary, people with closed mind will always think that they can do it by their principals. So, what kind of person are you?

Machine Learning for Audio, Image and Video Analysis

A hyperspectral index sensitive to subtle changes in the canopy chlorophyll content under arsenic stress

Support Vector Machine (SVM) for regression has recently attracted growing research interest due to its obvious advantage such as nonlinear function approximation with arbitrary accuracy, and good generalization ability, unique and globally optimal solutions. An overview of the basic ideas underlying SVM for regression is given in this paper. In particular, new methods such as n-SVM, LS-SVM, weighted SVM and linear SVM, and optimization algorithms including decomposition method and SMO and incremental learning with fast computational speed and ease of implementation are concentrated as well. SVM for regression is an important and promising new direction in the area of nonlinear parameter identification, forecast, modeling and control.

Support Vector Machines for Regression

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Support Vector Machines for Regression