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With the rapid growth of Internet, the network resource is increasing explosively. Information retrieval is one of main purposes as we browse Internet. At present, there are many retrieval methods and retrieval tools in information retrieval field, users can use all of these avenues to retrieve information. But how to increase the rapidity and precision has become the hotpot in this field. In this...
Clustering is one of the most fundamental and important problems in computer vision and pattern recognition communities. Maximum margin clustering (MMC) is a recently proposed clustering technique which has shown promising experimental results. The main theme behind MMC is to extend the standard maximum margin principle in support vector machine (SVM) to the unsupervised scenario. This paper will...
Detection of brain tumors from MRI is a time consuming and error-prone task. This is due to the diversity in shape, size and appearance of the tumors. In this paper, we propose a clustering algorithm based on Particle Swarm Optimization (PSO). The algorithm finds the centroids of number of clusters, where each cluster groups together brain tumor patterns, obtained from MR Images. The results obtained...
To understand customers' characteristics and their desire is critical for modern CRM (customer relationship management). The easiest way for a company to achieve this goal is to target their customers and then to serve them through providing a variety of personalized and satisfactory goods or service. In order to put the right products or services and allocate resources to specific targeted groups,...
The presence of a large number of available actions in the context of an automated, adaptive decision process can lead to an excessively large search space and thus significantly increase the overhead for the policy learning process. This issue occurs particularly in problem domains such as path planning or grid scheduling where the number of decision points is large and irreducible. The learning...
Virtual machine monitor provides the drastic improvement of isolation, consolidation and flexibility in running virtual machine. Also, virtual cluster becomes one of the hot topics for the combination of capacity planning, HPC (high performance computing) and virtualization technologies. In this paper we propose a dynamic protection system of Web server in virtual cluster using live migration. VMM...
Clustering is the process of gathering objects into groups based on their feature's similarity. In this paper, we concentrate on Weighted Kernel K-Means method for its capability to manage nonlinear separability and high dimensionality in the data. A new slight modification of WKM algorithm has been proposed and tested on real Rice data. The results show that the accuracy of proposed algorithm is...
Mean shift spectral clustering (MSSC) brings us an alternative for image segmentation. However, owing to being based on the classical Parzen window estimator (PW) and employing the full data sample for density estimation, the usefulness of MSSC is weakened. In this paper, the improved mean shift spectral clustering (IMSSC) algorithm is proposed by replacing PW with the reduced set density estimator...
A constructing method of fuzzy classifier using kernel k-means clustering algorithm is introduced in this paper. This constructing method are divided into three phases, namely clustering phase, fuzzy rule created phase and parameters modified phase. Firstly, the original sample space is mapped into a high dimensional feature space by selecting appropriate kernel function. In the feature space, training...
Semi-supervised clustering takes advantage of a small amount of labeled data to bring a great benefit to the clustering of unlabeled data. Based on a novel kernel method for clustering using one-class support vector machine, this paper presents two novel kernel-based semi-supervised clustering methods inspired by two semi-supervised variants of the k-means clustering algorithm by seeding respectively...
In kernel-based algorithms, Mercer kernel techniques have been used for improving the separability of input patterns. Although designed to tackle the problem of curse of dimensionality, non-accelerated kernel-based clustering algorithms fail to provide enough time efficiency for practical applications, such as medical image segmentation. For improving the time efficiency of kernel-based clustering,...
In this paper, a novel learning method based on kernelized fuzzy clustering and least squares support vector machines (LSSVM) is presented to improve the generalization ability of a Takagi-Sugeno-Kang (TSK) fuzzy modeling. Firstly, the fuzzy partition of the product space of input and output is obtained by kernelized fuzzy clustering. Then, a computationally efficient numerical method is proposed...
Takagi-Sugeno models are an important class of fuzzy rule based oriented models, generally used for prediction and control. Fuzzy clustering is one of effective methods for identification. In this method, we propose to use a fuzzy clustering method (Kernel based fuzzy c-means method) for automatically constructing a multi-input fuzzy model to identify the structure of a fuzzy model. To clarify the...
In this paper, we investigate the parallel training strategy and propose a parallel support vector regression machine algorithm that integrates model segmentation and data space decomposition. The major aim is to explore the new data space decomposition scheme that can solve computation intensive problem about the long time training based on SVR's classification by using low-dimension algorithms....
This paper presents a novel kernel density estimation approach to vehicle trajectory learning and motion analysis. The framework comprises a training stage and a testing stage. In the training stage, vehicle trajectories are first clustered by the hierarchical spectral clustering method. Then, through the proposed kernel density estimation approach, the average kernel density of one point on a trajectory...
Aiming at the problem of object-based image retrieval, a novel semi-supervised multi-instance learning (MIL) algorithm based on RS (rough set) attribute reduction and transductive support vector machine (TSVM) has been presented-RSTSVM-MIL algorithm. This algorithm regards the whole image as a bag, and the low-level visual feature of the segmented regions as instances, in order to transform every...
In order to improve the accuracy of multi-spectra remote sensing image classification, a terrain classification method based on support vector machine is proposed. A remote sensing image classification method based on SVM algorithm of C-SVC type is introduced and emphasis is put on the study of the improved SMO algorithm. In order to improve efficiency of classification, multiple-spectra remote sensing...
Line segment based representation of 2D robot maps is known to have advantages over raw point data or grid based representation gained from laser range scans. It significantly reduces the size of the data set. It also contains higher geometric information, which is necessary for robust post processing. The paper describes an algorithm to convert global 2D robot maps to line segment representation,...
This paper proposes an efficient approach for object classification. This method bases on bag-of-features classification framework and extends the limits of it. It applies modified spatial PACT as local feature descriptor, which can efficiently catch image patch's characteristic. In order to address the speed bottleneck of codebook creation, extremely randomized clustering forest is used to create...
Recently, keypoint descriptors such as Scale Invariant Feature Transform (SIFT) have been proved promising in similarity retrieval of images, which adopts matching score as similarity. However, the matching score is easy to be decreased once there are little variances between image details, and hence lead to low retrieval performance. In this paper, we propose a novel retrieval approach that improves...
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