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Data association and soft data streams
2010 13th International Conference on Information Fusion, 2010This paper discusses the challenges of and possible methods for data association in the domain of counterinsurgency where “soft/linguistic” data is an important input data type. An overview of the processing operations from input to construction of fused estimates is described.
Megan Hannigan +3 more
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Associative Classifier for Evidential Data
2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI), 2020Associative classifiers are a family of classification techniques that use association rules to classify a new instance. Many works were proposed in this context, with a focus on how to select and combine the relevant association rules for classification. However, most of them do not consider the imperfection of data.
Nassim Bahri +2 more
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A neural network for data association
1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999This paper presents a new neural solution for solving the data association problem. This problem, also known as the multidimensional assignment problem, arises in data fusion systems like radar and sonar targets tracking, robotic vision... Since it leads to an NP-complete combinatorial optimization, the optimal solution can not be reached in an ...
Michel Winter, Gérard Favier
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Modeling the evolution of associated data
Data & Knowledge Engineering, 2010Statistical topic models have been proposed for modeling documents and authorship information. However, few previous works have studied the evolution of associated data. In this paper, we investigate how to model trends of changes in document content and author interests simultaneously over time.
Jie Tang 0001, Jing Zhang 0001
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A measure of association for complex data
Computational Statistics & Data Analysis, 2003zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Seung-Chun Lee, Moon Yul Huh
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Associative Classifier for Uncertain Data
2010Associative classifiers are relatively easy for people to understand and often outperform decision tree learners on many classification problems. Existing associative classifiers only work with certain data. However, data uncertainty is prevalent in many real-world applications such as sensor network, market analysis and medical diagnosis.
Xiangju Qin +3 more
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Energy Association Filter for Online Data Association with Missing Data
2008Data association problem is of crucial importance to improve online object tracking performance in many difficult visual environments. Usually, association effectiveness is based on prior information and observation category. However, some problems can arise when objects are quite similar.
El Abed, Abir +2 more
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Integrated probabilistic data association
IEEE Transactions on Automatic Control, 1994Summary: The authors present an integrated probabilistic data association algorithm which provides recursive formulas for both data association and track quality (probability of track existence), allowing track initiation and track termination to be fully integrated into the association and smoothing algorithm. Integrated probabilistic data association
Darko Musicki +2 more
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An Idea for Quantization with Data Association
Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., 2006Quantization for estimation is explored for the case that it must be performed jointly with data association; that is, the case in which measurements are of uncertain origin. Data association requires some sort of gating of distributed observations, and a censoring strategy is proposed.
Stefano Maranò 0001 +2 more
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Data association with ambiguous measurements
2008 American Control Conference, 2008We address the problem of tracking a single object in the neighborhood of several other closely spaced, similar objects where the sensor used to do the tracking may randomly measure the wrong object. Unlike many tracking scenarios, there is no other environmental clutter producing additional erroneous measurements.
Matthew J. Travers +2 more
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