Titlebook: Advances in Intelligent Signal Processing and Data Mining; Theory and Applicati Petia Georgieva,Lyudmila Mihaylova,Lakhmi C Jain Book 2013

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書目名稱Advances in Intelligent Signal Processing and Data Mining被引頻次




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書目名稱Advances in Intelligent Signal Processing and Data Mining讀者反饋學(xué)科排名

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Computational Intelligence in Automotive Applications,ane detection algorithms can be separated into lane modeling, feature extraction and model parameter estimation. Each of these steps is discussed in detail with examples and results. A recently proposed lane feature extraction approach, which is called the Global Lane Feature Refinement Algorithm (G

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Detecting Anomalies in Sensor Signals Using Database Technology,cessed by tracking systems which allows for enhancing the provided kinematic information. In high-level fusion systems, this kinematic information can be combined with additional domain-specific data which allows for detecting object behavior and threat patterns. These systems contribute to situatio

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Hierarchical Clustering for Large Data Sets,ated and the scaling of hierarchical clustering in time and memory is discussed. A new method for speeding up hierarchical clustering with cluster seeding is introduced, and this method is compared with a traditional agglomerative hierarchical, average link clustering algorithm using several interna

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A Novel Framework for Object Recognition under Severe Occlusion,he comparison of assemblies of image regions with a previously stored view of a known prototype. Shape context representation and matching are employed for recovering point correspondences between the image and the prototype. Assuming that the prototype view is sufficiently similar in configuration

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Historical Consistent Neural Networks: New Perspectives on Market Modeling, Forecasting and Risk Anigh degree of nonlinearity. In this chapter we deal with a special type of time-delay recurrent neural networks. In these models we understand a part of the world as a large recursive system which is only partially observable. We model and forecast all observables, avoiding the problem in open syste

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