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In view of the problem that support vector machine classifier learning new knowledge is poor in real time, a new algorithm of radar emitter identification based on hull vector and Parzen window density estimation is studied. The algorithm takes the Parzen window density estimation to eliminate the outliers, and reduces the training time by using the hull vector of the sample set. The identification...
A novel online, i.e. stochastic gradient, learning algorithm in a primal domain is introduced and its performance is compared to the Sequential Minimal Optimization (SMO) based algorithm for training L1 Support Vector Machines (SVMs) implemented within MATLAB's SVM solver fitcsvm. Their performances are compared on both real and artificial datasets, which contain up to 15,000 samples. These datasets...
Support vector ordinal regression (SVOR) is a popular method to tackle ordinal regression problems. However, until now there were no effective algorithms proposed to address incremental SVOR learning due to the complicated formulations of SVOR. Recently, an interesting accurate on-line algorithm was proposed for training $\nu $ -support vector classification ($\nu $ -SVC), which can handle a quadratic...
In many Natural Language Processing tasks, kernel learning allows to define robust and effective systems. At the same time, Online Learning Algorithms are appealing for their incremental and continuous learning capability. They allow to follow a target problem, with a constant adaptation to a dynamic environment. The drawback of using kernels in online settings is the continuous complexity growth,...
Most visual surveillance and video understanding systems require knowledge of categories of objects in the scene. One of the key challenges is to be able to classify any object in a real-time procedure in spite of changes in the scene over time and the varying appearance or shape of object. In this paper, we explore the applications of kernel based online learning methods in dealing with the above...
The Support Vector Machine (SVM) methodology is an effective, supervised, machine learning method that gives state-of-the-art performance for brain state classification from functional magnetic resonance brain images (fMRI). Due to the poor scalability of SVM (cubic in the number of training points) and the massive size of fMRI images, a SVM analysis is usually performed after data collection. Recent...
A fast online algorithm OnlineSVMR for training Ramp-Loss Support Vector Machines (SVMRs) is proposed. It finds the optimal SVMR for t + 1 training examples using SVMR built on t previous examples. The algorithm retains the Karush-Kuhn-Tucker conditions on all previously observed examples. This is achieved by an SMO-style incremental learning and decremental unlearning under the Concave-Convex Procedure...
During last few years, a number of kernel-based online algorithms have been developed that have shown better performance on a number of tasks. A well designed online algorithm needs less computation to reach the same test accuracy as the corresponding batch algorithm. In this paper, we devise an online training algorithm for L2-SVM. Our work is motivated by HULLER, an online algorithm proposed by...
We present a distributed machine learning framework based on support vector machines that allows classification problems to be solved iteratively through parallel update algorithms with minimal communication overhead. Decomposing the main problem into multiple relaxed subproblems allows them to be simultaneously solved by individual computing units operating in parallel and having access to only a...
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