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As a new information acquisition and processing technology, wireless sensor networks can achieve tasks with complex large-scale monitoring and tracking in wide range of applications, but the localization for node itself is the basis of most applications. APIT is a major localization algorithm of ranged free algorithms, but the algorithm has the problems of big localization errors and low coverage...
Location based services (LBS) is one of the fastest growing areas in recent years. Location update of mobile clients is fundamental in all types of LBS. But algorithms proposed in this field generally didn't concern the restricted context of road network and caused some unnecessary update. This paper proposed a revised vector-based update algorithm which taking characteristics of road network into...
Process Neural Network (PNN) has an important significance in solving industry modeling problems which are related to time, but long time is cost on high dimension inputs nonlinear modeling problems. A new Improved Process Neural Networks based on KPCA and Walsh (IPNN-KPW) are proposed in this paper. KPCA method and discrete Walsh transform are used to reduce process neural network's time cost. Momentum...
We study the problem of detecting and profiling terrorists using a combination of an ensemble classifier, namely random forest and relational information. Given a database for a set of individuals characterized by both "local" attributes such as age and criminal background, and "relational" information such as communications among a subset of the individuals, with a subset of the...
Support vector machine (SVM) has been a promising method for data mining and machine learning in recent years. However, the training complexity of SVM is highly dependent on the size of a data set. A cluster support vector machines (C-SVM) method for large-scale data set classification is presented to accelerate the training speed. By calculating cluster mirror radius ratio and representative sample...
Support vector machine (SVM) has been a promising method for data mining and machine learning in recent years. However, the training complexity of SVM is highly dependent on the size of a data set. A preprocessing support vector machines (P-SVM) method for large-scale data set classification is presented to speed up SVM training. By analyzing the neighbor classification feature for each sample in...
We study the problem of detecting and profiling terrorists using a combination of ordinary flat classifiers and relational information. Our starting point is a database for a set of individuals characterized by both ldquolocalrdquo attributes such as age and criminal background, and ldquorelationalrdquo information such as communications among a subset of the individuals. A subset of the individuals...
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