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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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樓主: 大腦
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發(fā)表于 2025-3-28 14:52:15 | 只看該作者
https://doi.org/10.1007/978-3-658-24355-5he-art of localization techniques. Next, it formulates the problem of localization within Bayesian framework and presents sequential Monte Carlo methods for localization based on received signal strength indicators (RSSIs). Multiple model particle filters are developed and their performance is evalu
42#
發(fā)表于 2025-3-28 21:25:12 | 只看該作者
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發(fā)表于 2025-3-29 01:02:53 | 只看該作者
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發(fā)表于 2025-3-29 04:05:17 | 只看該作者
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發(fā)表于 2025-3-29 09:20:34 | 只看該作者
Constraining Shape and Size in Clusteringated 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
46#
發(fā)表于 2025-3-29 11:55:52 | 只看該作者
Event Detection in Environmental Scanninghe 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
47#
發(fā)表于 2025-3-29 18:53:38 | 只看該作者
A. Sch?nhuth,I. G. Costa,A. Schliepigh 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
48#
發(fā)表于 2025-3-29 21:03:55 | 只看該作者
Peter M. Kappeler,Carel P. Schaikbased on interactions between an agent and its environment. Through repeated interactions with the environment, and the receipt of rewards, the agent learns which actions are associated with the greatest cumulative reward..This work describes the computational implementation of reinforcement learnin
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