The original support vector machine (SVM) uses the hinge loss function, which is non-differentiable and makes the problem difficult to solve in particular for regularized SVM, such as with ℓ1-regularized. On the other hand, the hinge loss is sensitive to noise. To circumvent these drawbacks...
Original paper: Classification of foreign fibers in cotton lint using machine vision and multi-class support vector machinedoi:10.1016/J.COMPAG.2010.09.002Daoliang LiWenzhu YangSile WangElsevierComputers and Electronics in AgricultureLi, D.; Yang, W.; Wang, S. Classification of foreign fibers in...
This paper deals with the application of a novel neural network technique, support vector machine (SVM), in financial time series forecasting. The objective of this paper is to examine the feasibility of SVM in financial time series forecasting by comparing it with a multi-layer back-propagation...
support vector machinesegmentationp class=MsoNormal style=text-align: left; margin: 0cm 0cm 0pt; layout-grid-mode: char; align=leftspan class=textspan style=font-family: ;Arial;,;sans-serif;; font-size: 9pt;Surface reconstruction is one of the main parts of reverse engineering and ...
SVM classifiers are machine-learning tools built to predict the class or category to which a particular object belongs as a function of ann-dimensional feature vector (χ). They are constructed adjusting by training the parameters of a classification function (Eq.1) to get an optimal classificatio...
This paper presents a new method to simulate complex land use systems by integrating support vector machine(SVM),cellular automata,and GIS.Recently,cellular automata(CA) have been increasingly used to dynamically simulate urban growth and land use.There are many issues that should be solved in the...
Kernel-based machine learning algorithms are based on mapping data from the original input feature space to a kernel feature space of higher dimensionality to solve a linear problem in that space. Over the last decade, kernel based classification and regression approaches such as support vector mach...
Support Vector Machine Feature Selection Antisense Oligonucleotide Input Feature Wrap MethodDownload PDF Sections Figures References Abstract Background Methods Results Conclusions References Author information Additional information Authors’ original submitted files for images Rights and permissions About this arti...
Finally, the kernel functions with a support vector machine (SVM) are applied to the feature vectors to predict the compound activity. The relationship matrices are used to extract the significant sub-paths from the original paths of stars. The proposed algorithm uses the relationship matrices to ...
Hyperplane based support vector machine As described above in Section 1, various different methods have been developed to improve the performance of SVM. In this paper, the concept of distance to hyperplane is introduced to measure the probability of a sample data of being considered as a support...