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We explore the use of todaypsilas high-end graphics processing units on desktops to perform hierarchical agglomerative clustering with the compute unified device architecture - CUDA of NVIDIA. Although the advancement in graphics cards has made the gaming industry to flourish,there is a lot more to be gained the field of scientific computing, high performance computing and their applications. Previous...
The paper gives a validity analysis on an automatic dynamic electrocardiogram (Holter) waveform selection strategy. The strategy was based on machine learning techniques. And the data used in analysis are from clinic. The analysis showed that 93% can be reached in clustering phase, and 92% in classification phase. Although the result was not very satisfied, it was a good trying in this study area...
Source IP addresses are often used as a major feature for user modeling in computer networks. Particularly in the field of distributed denial of service (DDoS) attack detection and mitigation traffic models make extensive use of source IP addresses for detecting anomalies. Typically the real IP address distribution is strongly undersampled due to a small amount of observations. Density estimation...
Based on a distance of kernel method, a novel noise-resistant fuzzy clustering algorithm called kernel noise clustering (KNC) algorithm, is proposed. KNC is an extension of the noise clustering (NC) algorithm proposed by Dave. By replacing the Euclidean distance used in the objective function of NC algorithm, a new distance is introduced in NC algorithm. The distance of the kernel method is more robust...
A novel approach for the classification of compressed video data using centroid neural network with Bhattacharyya kernel (CNN(BK)) is proposed in this paper. The proposed classifier is based on centroid neural network (CNN) and also exploits advantages of the kernel method for mapping input data into a higher dimensional feature space. Furthermore, since the feature vectors of compressed video data...
This paper presents a new algorithm named kernel bisecting k-means and sample removal (KBK-SR) as a sampling preprocessing for SVM training to improve the scalability. The novel clustering approach kernel bisecting k-means in the KBK-SR tends to fast produce balanced clusters of similar sizes in the kernel feature space, which makes KBK-SR efficient and effective for reducing training samples for...
A denoising algorithm for point-sampled geometry is proposed based on noise intensity. The noise intensity of each point on point-sampled geometry (PSG) is first measured by using a combined criterion. Based on mean shift clustering, the PSG is then clustered in terms of the local geometry-features similarity. According to the cluster to which a sample point belongs, a moving least squares surface...
How to deal with the very large database in decision-making applications is a very important issue, which sometimes can be addressed using SVMs. This paper presents a new sample reduction algorithm as a sampling preprocessing for SVM training to improve the scalability. We develop a novel top-down kernel clustering approach which tends to fast produce balanced clusters of similar sizes in the kernel...
A fast fractal encoding algorithm based on ant colony algorithm is proposed to reduce coding time. The algorithm produces a completely identical fractal encoding to that of the conventional full search in reduced time. Using ant-based clustering algorithm and kernel method, we propose in this paper a kernel function clustering based on ant colony algorithm. It automatically realizes classification...
This paper presents a simplified support vector clustering (SVC) algorithm for improving the efficiency of the SVC training procedure. The cluster structure obtained by our proposed approach is controlled by two parameters: the parameter of kernel functions, denoted as q; and the percentage of data used to form the contour. The mechanisms we developed can efficiently search for suitable parameters...
Search Engine has proven its effectiveness for retrieval of information from World Wide Web. Traditionally, the search results are arranged in an ordered list by popularity and relevancy. However, the enormous size of matched Web pages causes inefficiency for users to locate the most relevant Web pages. A proper organization of the search result is important to improve its browsability of Web searching...
Several problems are existed when K-NN (K- nearest neighbor) method is used to classify the Holter waveforms: the data scale is too large; the classification algorithm needs training samples; the K-NN is a linear classification method. Therefore, this paper proposes a new K-NN algorithm; the algorithm is based on kernel function. Through this change, classification is transformed from linear to non-linear...
In this paper, a new style radial basis function neural network (RBF NN) is used for fault diagnosis in Particleboard Glue Mixing & Dosing System, which is firstly used in this field. The structure and its training algorithm of the network are discussed and the training algorithm chosen in the article is a self-adapt clustering training algorithm. The results of the simulation and fault tolerance...
A great challenge of text mining arises from the increasingly large text datasets and the high dimensionality associated with natural language. In this research, a systematic study is conducted in the context of the document clustering, using the recently introduced diffusion framework and some characteristics of the singular value decomposition. This study is three-fold. First, we propose to construct...
Object recognition and categorization are considered as fundamental steps in the vision based navigation for inspection robot as it must plan its behaviors based on various kinds of obstacles detected from the complex background. However, current approaches typically require some amount of supervision, which is viewed as a expensive burden and restricted to relatively small number of applications...
Clustering with constraints is an active area in machine learning and data mining. In this paper, a semi-supervised kernel-based fuzzy C-means algorithm called PCKFCM is proposed which incorporates both semi-supervised learning technique and the kernel method into traditional fuzzy clustering algorithm. The clustering is achieved by minimizing a carefully designed objective function. A kernel-based...
Kernel k-means is an extension of the standard k-means clustering algorithm that identifies nonlinearly separable clusters. In order to overcome the cluster initialization problem associated with this method, in this work we propose the global kernel k-means algorithm, a deterministic and incremental approach to kernel-based clustering. Our method adds one cluster at each stage through a global search...
A natural Euclidean space is defined on a set of texts as sequences or hierarchical structures. Unlike the traditional term-document model, the present model takes local topological structure of texts. Kernel functions are defined that enable the use of Euclidean spaces and hence methods of data analysis based on kernels are applicable to the present model. Applications include agglomerative as well...
Time series clustering finds applications in diverse fields of science and technology. Kernel based clustering methods like kernel K-means method need number of clusters as input and cannot handle outliers or noise. In this paper, we propose a density based clustering method in kernel feature space for clustering multivariate time series data of varying length. This method can also be used for clustering...
This paper researches the possibility of using locally weighted algorithm for intelligent modeling of a nonlinear system for vanadium extraction in metallurgical process and proposes some optimized methods by finding the optimized regression coefficients by gradient descent and kernel function bandwidth by weighted distance. But kernel matrix computation for high dimensional data source demands heavy...
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