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Development of smart cities has grasped much attention in research community and industry as well. Smart healthcare, communication, infrastructure are required for the development of smart cities. Security is one of the major concern in the development of smart cities. Automatic surveillance helps in boosting security in multiple areas like traffic, hospitals, schools, and industries etc. Video camera...
We consider a task of classifying normal and pathological brain networks. These networks (called connectomes) represent macroscale connections between predefined brain regions, hence, the nodes of connectomes are uniquely labeled and the set of labels (brain regions) is the same across different brains. We make use of this property and hypothesize that connectomes obtained from normal and pathological...
The disadvantages of BOW (Bag of words model) for image classification include the large amount of data in generating a codebook by clustering, redundant code words that may affect the classification results and so on. The process of BOW for the classification can be improved through the Laplace weights to improved fuzzy C means algorithm, and obtaining codebook with more ability to distinguish between...
Waveform decomposition is an important step in full-waveform LiDAR remote sensing. Under the Gaussian Mixture Model, the conventional parametric classification algorithm of Expectation-Maximization (EM) is among the most widely applied ones to decompose the waveforms. This paper introduces nonparametric classification methods, such as K-means and mean-shift to decompose the LiDAR waveforms. The experiments...
This paper presents an approach for classification which is based on the neighborhood expansion. The proposed algorithm can (1) find automatically the number of clusters, and (2) classify irregular data set. In the approach, we first defined the distance between a point and a set, then the neighborhood of a data set. The algorithm can begin with any point in the data set and expands the point to a...
A number of clustering algorithms can be employed to find clusters in multivariate data. However, the effectiveness and efficiency of the existing algorithms are limited, since the respective data has high dimension, contain large amount of noise and consist of clusters with arbitrary shapes and densities. In this paper, a new kernel density-based clustering algorithm, called Local Triangular Kernel-based...
Non-negative factorization (NMF) has been a popular machine learning method for analyzing microarray data. Kernel approaches can capture more non-linear discriminative features than linear ones. In this paper, we propose a novel kernel NMF (KNMF) approach for feature extraction and classification of microarray data. Our approach is also generalized to kernel high-order NMF (HONMF). Extensive experiments...
A novel approach to active sampling is proposed for the semi automatic selection of training patterns in a given pool of candidates. In the method proposed, each candidate is ranked with a double criterion: first, informativeness of the candidate is assessed using the Support Vector Machine (SVM) real valued decision function. Then, diverse sampling is ensured by considering the relative position...
Image segmentation and unsupervised classification are difficult problems. We propose to combine both. A clustering process is applied over segment mean values. Only large segments are considered. The clustering is composed of a mean-shift step and a hierarchical clustering step. The hierarchical grouping is based upon a powerful segmentation technique previously developed. The approach is applied...
This paper presents the support vector machine (SVM) for classification of the quality grade of knitted yarns. The SVM, Kernel Fisher Discriminant Analysis (KFDA), back promulgation neural network (BPNN), and radial basis function neural network (RBFNN) are comparatively investigated in 94 classified knitted yarns from different mills in four-dimensional space, four methods are employed on IRIS and...
In this paper, we propose a method for automatic classifying fragments in computer-aided restoration of ceramic cultural relics, using surface texture clustering. Firstly, center of clustering will be initialized by re-division on multi-variant finite model. Then kernel-based fuzzy clustering algorithm is applied and parameters of difference degree are used to control iterations of clustering. The...
On the base of NS-IMMC, this paper propose a new method of generating the cause-and-effect of news topic. The new method choose representative sentences for news documents according to the specialty of news structure (NS, News structure), and then utilizes IMMC (Improved Min-Max clustering) to classify these representative sentences to generate multi-documents summary which represents the topic cause-and-effect...
Classification modeling in data mining has evolved since 1990psilas. Many methods have been introduced and experimented. Among them were Multi Layer Perceptron and Radial Basis Function in Neural Network and Multiple Regressions in Statistical Analysis. Not many researches have been proposed in the field of rough classification modeling. When SIP/DRIP algorithm was ported on rough classification model,...
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