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As a widely used medium platform, Micro-blog influence research is a hotspot. The community micro-blog, which is used as an effective tool by social managers in virtual community, has developed rapidly in recent years. As the basis of government micro-blog system in China, the community blog influence has great importance to guide the public popular feelings and guarantee the safety of the virtual...
Extraction of the essential features from massive bands is a key issue in hyperspectral images classification. Plenty of feature extraction techniques can be found in the literature but most of these methods rely on the linear/stationary assumptions. The aim of this paper is to propose an alternative methodology based on the ensemble empirical mode decomposition (EEMD) and utilize the versatile support...
For most credit risk assessment models, decision attributes and history data are of great importance in terms of accuracy of prediction. Decision attributes can be classified into two types: numerical and categorical. As these two types have different characteristics, there will be interference if they are used simultaneously in the same model. By applying the case based reasoning (CBR) and artificial...
Many forecasting models have been developed for forecasting wind farm electricity output. In most situations, performance of models is problem-dependent. Thus, it is difficult for forecasters to choose the right technique for each unique situation. In order to overcome this problem, this paper integrates multiple models into an aggregated model to obtain further performance improvement. Firstly, three...
Using melody and/or lyric to query a music retrieval system is convenient for users but challenging for developers. This paper proposes efficient schemes for realizing key algorithms in such a kind of system. Specifically, we characterize our system by adding lyric to query as follows: A Support Vector Machine (SVM) is employed to distinguish humming queries from singing queries, For a singing query,...
Bi-dimensional empirical mode decomposition (BEMD) has been one of the core activities in image processing. Unfortunately, this promising technique is sensitive to boundary effect. Here, a new technique based on multivariate grey model termed as GM(1, 3) is developed for boundary extension in BEMD. More specifically, pixel values and coordinates of the image are regarded as characteristic data series...
The mathematical modeling of classifier has been intensively investigated in pattern recognition for decades. Maximin classifier, which conducts optimization based on the perpendicularly closest data point(s) to the decision boundary, has been widely used. However, such method may lead to inferior performance when the boundary data point(s) is significantly influenced by noise. This paper presents...
Sparse representation has been introduced to address many recognition problems in computer vision. In this paper, we propose a new framework for object categorization based on sparse representation of local features. Unlike most of previous sparse coding based methods in object classification that only use sparse coding to extract high-level features, the proposed method incorporates sparse representation...
The green supplier evaluation is of great importance for the green supply chain management. Unlike other industries, the thermal power supply chain has its own characteristic, therefore the green coal suppler evaluation models should differ from those of other industry. In this paper, an evaluation index system for green coal suppler is established according to the characteristic of the thermal power...
Numerous digital cameras and modern phones have a face detection module, which is used to automatically focus (AF) and optimize exposure (AE). But the face detection will fail when person doesn't face the camera or the part of the face is occluded. In order to avoid such problems, we propose a fast head-shoulder detector, which uses Variable-size block Histograms of Orientated Gradients (VHOG) descriptors...
This paper proposes a feature relation network (FRN) to model the underlying feature relation structures of a set of observations. A pattern classification system is then constructed based on the feature relation network, namely PCS-FRN. During training process, PCS-FRN will form an attractor for each group of samples in order to lower the overall energy states. The attractor, or a feature relation...
In this paper, support vector machine (SVM) have been applied to the failure probabilistic model of CNC lathe, and two methodologies (SVM and least square method) have been compared for the analysis of the failure data collected from eighty CNC lathes. The proposed failure probabilistic model based on SVM was more accurate and reliable than that of least square. Hence, the more accurate mean time...
Climate is a nonlinear system, and the BP neural network algorithm or the Support Vector Machine (SVM) algorithm which is superior in dealing with nonlinear problems is usually used in the climate forecast. Meanwhile, the climatic time series also include nonstationary feature, so this paper introduces a new method of signal processing-the Empirical Mode Decomposition (EMD) algorithm for making climatic...
Based on the framework of support vector machines (SVM) using one against one (OAO) strategy, a new kernel method based on Bhattacharyya distance is proposed to raise the classification accuracy by combining the characteristics of hyperspectral data. The proposed method takes advantage of the non-uniform information distribution of hyperspectral data and makes the band with greater separability play...
Ontology learning aims to facilitate the construction of ontologies by decreasing the amount of effort required to produce an ontology for a new domain. However, there are few studies that attempt to automate the entire ontology learning process from the collection of domain-specific literature, to text mining to build new ontologies or enrich existing ones. In this paper, we present a complete framework...
Support vector machine (SVM) appears to be a robust alternative for pattern recognition with hyperspectral data. However, this kernel-based method does not take into consideration the bio-physical meaning of the spectral signatures. Observation of real-life spectral signatures from the AVIRIS hyperspectral dataset shows that the useful information for classification is not equally distributed across...
Ontology learning has become a major area of research whose goal is to facilitate the construction of ontologies by decreasing the amount of effort required to produce an ontology for a new domain. However, there are few studies that attempt to automate the entire ontology learning process from the collection of domain-specific literature, to text mining to build new ontologies or enrich existing...
Decision tree support vector machine (DTSVM), which combines SVM and decision tree using the concept of dichotomy, is proposed to solve the multi-class fault diagnosis tasks. Since the classification performance of DTSVM is closely related to its structure, genetic algorithm is introduced into the formation of decision tree, to cluster the multi-classes with maximum distance between the clustering...
This paper proposes a novel modeling technique for understanding cancer signal pathway and applies to cancer classification. In the approach, specific to a cancer group, a regulatory network is constructed between biomarkers and is optimized towards minimizing its energy function that is defined as disagreement between input and output of the network. The non-linear version of this network is achieved...
Multi-sensor data fusion and network situation awareness are emerging technique in the field of network security and they help administrators to be aware of the actual security situation of their networks. This paper mainly focuses on heterogeneous multi-sensor data fusion and situation awareness. We adopted Snort and NetFlow collector as two sensors to gather real network traffic and fused them use...
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