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Despite the importance of distributed learning, few fully distributed support vector machines exist. In this paper, not only do we provide a fully distributed nonlinear SVM; we propose the first distributed constrained-form SVM. In the fully distributed context, a dataset is distributed among networked agents that cannot divulge their data, let alone centralize the data, and can only communicate with...
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...
Stochastic Gradient Descent (SGD) is the best known method to optimize the primal objective for linear support vector machines (SVM) to dispose large data. However, when equipped with kernel functions, SGD performance is vulnerable that causes unbounded linear growth in model size and update time with data size. This paper describes a budgeted parallel pack gradient descent algorithm (BPPGD) that...
Identification of high risk among viruses is crucial for interpreting its oncogenic mechanisms. Also it is gainful in developing ultra modern clinical tools for its diagnosis, prevention and treatment. Prediction of the structure of a particular virus helps to classify the risk type of that virus. Prediction methods based on text based classification do not provide satisfactory results as compared...
For hyperspectral image classification, feature extraction is a crucial pre-process for avoiding the Hughes phenomena. Some feature extraction methods such as linear discriminant analysis (LDA), nonparametric weighted feature extraction (NWFE), and their kernel versions, generalized discriminant analysis (GDA) and kernel nonparametric weighted feature extraction method (KNWFE) have been shown that...
Capturing human movement has become available in detail due to the advancement of motion sensor technology integrated by micro-machine and also due to the one of optical recording by high speed and high resolution image sensors. Therefore, we can easily record the human activity as the body movement BigData and analyze it to quest skill to become an expert of a target body movement. Especially, in...
Despite the abnormal patterns recognition and mean shift size estimation of control chart signals could provide some evidence for statistical process diagnostics, it do not reveal the real time of the process changes, which is essential for identifying assignable causes and ultimately ensure stability of process. In this paper, a support vector machine based multi-kernel (MK-SVM) method for change...
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...
Recently, sparse representation based classification (SRC) and collaborative representation based classification (CRC) have achieved superior performance in pattern classification. Collaborative representation based classification with regularized least square (CRC_RLS) which uses l2 -norm is a very simple yet much more efficient scheme for face recognition (FR). Motivated by the fact that kernel...
In the Smart grid, network security is the important part. In this paper, we will introduce a new method detection based on Support Vector Machines to detect Masquerade attack, and test it and other methods on the dataset from keyboard commands on a UNIX platform. The presence of shared tuples would cause many attacks in this dataset to be difficultly detected, just as other researchers shown. In...
This paper presents an iterative classification algorithm called Ridge-adjusted Slack Variable Optimization (RiSVO). RiSVO is an iterative procedure with two steps: (1) A working subset of the training data is selected so as to reject “extreme” patterns. (2) the decision vector and threshold value are obtained by minimizing the energy function associated with the slack variables. From a computational...
Novelty detection plays an important role in machine learning and signal processing. This paper studies novelty detection in a new setting where the data object is represented as a bag of instances and associated with multiple class labels, referred to as multi-instance multi-label (MIML) learning. Contrary to the common assumption in MIML that each instance in a bag belongs to one of the known classes,...
Principal component analysis (PCA) and kernel principal component analysis (KPCA) are widely used approaches of dimensionality reduction. They have been demonstrated useful for gearbox fault diagnosis. This paper provides a brief review of applications of PCA and KPCA for gearbox fault diagnosis. Literature is mainly grouped into two categories: applications of the conventional PCA/KPCA and applications...
Recently, one popular discriminative training method for classifier design, Minimum Classification Error (MCE) training, has been significantly revised. This revision upgraded Large Geometric Margin Minimum Classification Error (LGM-MCE) training by embedding a kernel-based feature space projection mechanism. This latest MCE training is called Kernel Minimum Classification Error (KMCE) training and...
This paper considers feature selection in a multiclass classification scenario where the goal is to determine a subset of available features which is most discriminative and informative for all the classes simultaneously. Based on the data distributions of classes in the feature space, this paper first presents a model selection criterion named multiclass kernel polarization (MKP) to evaluate the...
The maxi-min margin machine (M4) algorithm, contrast to the traditional support vector machine (SVM) algorithm, gives a more robust solution and gets better generalization performance. In this paper we extend the M4 classification algorithm to deal with regression problem, and propose a novel regression method. This method inherits the characteristics of M4 such as good robustness and generalization...
One-class SVM, which is a good machine learning method, has recently attracted wide interest. In this paper, we focus on the properties of the optimal solution to its primal optimization problem. We first prove that the optimal solution with respect to the normal vector of the hyperplane is unique, and then present and prove the necessary and sufficient conditions for the case where the optimal solution...
Various distance measures have been proposed for time series classification, and several of them have been used to construct Gaussian distance kernels for support vector machine (SVM) - based classification. Considering that different Gaussian distance kernels may carry complementary information for classification, in this paper, we propose a multiple kernel learning (MKL) method to integrate multiple...
In this paper we propose a novel Support Vector Machine(SVM) based approach for noisy data removal from datasets. It is observed that the instability present in the dataset greatly affects the overall performance of the any classifier. Hence, we propose a methodology for removal of such instabilities. In the proposed approach, we proceed by determining the clusters formed using support equilibrium...
This paper introduces a simple yet powerful data transformation strategy for kernel machines. Instead of adapting the parameters of the kernel function w.r.t. the given data (as in conventional methods), we adjust both the kernel hyper-parameters and the given data itself. Using this approach, the input data is transformed to be more representative of the assumptions encoded in the kernel function...
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