Download Advances in Intelligent Signal Processing and Data Mining: by Petia Georgieva, Lyudmila Mihaylova, Lakhmi C Jain PDF

By Petia Georgieva, Lyudmila Mihaylova, Lakhmi C Jain

ISBN-10: 364228695X

ISBN-13: 9783642286957

ISBN-10: 3642286968

ISBN-13: 9783642286964

The ebook provides one of the most effective statistical and deterministic equipment for info processing and functions which will extract distinct details and locate hidden styles. The concepts awarded diversity from Bayesian methods and their adaptations corresponding to sequential Monte Carlo tools, Markov Chain Monte Carlo filters, Rao Blackwellization, to the biologically encouraged paradigm of Neural Networks and decomposition thoughts similar to Empirical Mode Decomposition, autonomous part research and Singular Spectrum research.

The booklet is directed to the study scholars, professors, researchers and practitioners attracted to exploring the complex suggestions in clever sign processing and information mining paradigms.

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Extra info for Advances in Intelligent Signal Processing and Data Mining: Theory and Applications

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45). 45). 6 A Unified Causal Reasoning and Tracking Paradigm In many practical applications the constituent underlying traits, which are represented here by the processes {xkj }nj=1 , may not be perfectly known (in the context of our work these could be the estimated object position and velocity, μˆ kj , μˆ˙ kj ). Hence instead of the actual traits one would be forced to use approximations that might not be consistent estimates of the original quantities. Y. Carmi et al. causal mechanism. A plausible approach for alleviating this problem is to introduce a compensated causal structure that takes into account the exogenous disturbances induced by the possibly inconsistent estimates.

14. In these videos, the actual leader (designated by a red shirt) performs a random trajectory, and the followers loosely follow its motion pattern. The clustering procedure described above is used to estimate the trajectories of the objects (the trajectories were filtered using a simple moving-average procedure to reduce the amount of noise contributed by the k-means clustering method). These trajectories were fed into the causality inference scheme. The results of this procedure are shown in Fig.

Z♠ Fig. 5. From left to right: depiction of the causal hierarchies (based on out degrees) (X,Y, Z), (Y, X, Z), and (Z,Y, X). The most influential agents in the causal diagrams from left to right are X, Y and Z, respectively. Inferring Causal Hierarchies by Other Methods To some extent, causal hierarchies can be inferred using the class of principal component analysis (PCA)-based methods. Probably the most promising one in the context of our problem is the multi-channel singular spectrum analysis (M-SSA), 2 Monte Carlo-Based Bayesian Group Object Tracking and Causal Reasoning 33 which is otherwise known as extended empirical orthogonal function (EEOF) analysis [84].

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Advances in Intelligent Signal Processing and Data Mining: Theory and Applications by Petia Georgieva, Lyudmila Mihaylova, Lakhmi C Jain

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