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One of the most current challenging problems in Gaussian process regression (GPR) is to handle large-scale datasets and to accommodate an online learning setting where data arrive irregularly on the fly. In this paper, we introduce a novel online Gaussian process model that could scale with massive datasets. Our approach is formulated based on alternative representation of the Gaussian process under...
Stochastic Gradient Descent (SGD) based method offers a viable solution to training large-scale dataset. However, the traditional SGD-based methods cannot get benefit from the distribution or geometry information carried in data. The reason is that these methods make use of the uniform distribution over the entire training set so as to sample the next data point for updating the model. We address...
Anomaly detection (AD) involves detecting abnormality from normality and has a wide spectrum of applications in reality. Kernel-based methods for AD have been proven robust with diverse data distributions and offering good generalization ability. Stochastic gradient descent (SGD) method has recently emerged as a promising framework to devise ultra-fast learning methods. In this paper, we conjoin the...
We present Mixture of Support Vector Data Descriptions (mSVDD) for one-class classification or novelty detection. A mixture of optimal hyperspheres is automatically discovered to describe data. The model consists of two parts: log likelihood to control the fit of data to model (empirical risk) and regularization quantizer to control the generalization ability of model (general risk). Expectation Maximization...
Linear Support Vector Machine (LSVM) has recently become one of the most prominent learning methods for solving classification and regression problems because of its applications in text classification, word-sense disambiguation, and drug design. However LSVM and its variations cannot adapt accordingly to a dynamic dataset nor learn in online mode. In this paper, we introduce an Adaptable Linear Support...
Support-based clustering method has recently drawn plenty of attention because of its applications in solving the difficult and diverse clustering or outlier detection problem. Support-based clustering method undergoes two phases: finding the domain of novelty and doing cluster assignment. To find the domain of novelty, the training time given by the current solvers is typically quadratic in the size...
Support vector machine (SVM) considers all data points with the same importance in classification problems, therefore SVM is very sensitive to noisy data or outliers. Current fuzzy approach to two-class SVM introduces a fuzzy membership to each data point in order to reduce the sensitivity of less important data, however computing fuzzy memberships is still a challenge. It has been found that the...
One of the important problems in medical imaging is two-class classification, for example determination of benign from malignant cases in breast cancer treatment. In this paper we present a new support vector machine method for two-class medical image classification. The key idea of this method is to construct an optimal hypersphere such that both the interior margin between the surface of this sphere...
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