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Another SVM version known as least-squares support vector machine (LS-SVM) has been proposed by Suykens and Vandewalle. ... JKernelMachines, OpenCV and others. ...
OpenCV (Open Source Computer Vision Library) is a library of programming functions mainly for real-time computer vision. [2] ... Support vector machine (SVM)
Since images are represented based on the BoW model, any discriminative model suitable for text document categorization can be tried, such as support vector machine (SVM) [2] and AdaBoost. [11] Kernel trick is also applicable when kernel based classifier is used, such as SVM. Pyramid match kernel is newly developed one based on the BoW model.
Ranking SVM; Regularization perspectives on support vector machines; S. Sequential minimal optimization; Structured support vector machine
The structured support-vector machine is a machine learning algorithm that generalizes the Support-Vector Machine (SVM) classifier. Whereas the SVM classifier supports binary classification, multiclass classification and regression, the structured SVM allows training of a classifier for general structured output labels.
SVM algorithms categorize binary data, with the goal of fitting the training set data in a way that minimizes the average of the hinge-loss function and L2 norm of the learned weights. This strategy avoids overfitting via Tikhonov regularization and in the L2 norm sense and also corresponds to minimizing the bias and variance of our estimator ...
In machine learning, kernel machines are a class of algorithms for pattern analysis, whose best known member is the support-vector machine (SVM). These methods involve using linear classifiers to solve nonlinear problems. [1]
Classification: K-means, SVM, Markov random fields and access to all OpenCV machine learning algorithms [10] Change detection [11] Stereo reconstruction from images; Orthorectification and map projections (using ossim) [12] Radiometric indices (vegetation, water, soil) [13] Object-based segmentation and filtering; PCA computation