When we mine information for knowledge on a whole data streams it's necessary to cope with uncertainty as only a part of the stream is available. We introduce a stastistical technique, independant from the used al...
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the task of extracting knowledge from text is an important research problem for information processing and document understanding. Approaches to capture the semantics of picture objects in documents constitute subject...
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the work presented in this paper is part of the cooperative research project AUTO-OPT carried out by twelve partners from the automotive industries. One major work package concerns the application of data mining metho...
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the real transactional databases often exhibit temporal characteristic and time varying behavior. Temporal association rule has thus become an active area of research. A calendar unit such as months and days, clock un...
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Assessing the similarity between objects is a prerequisite for many data mining techniques. this paper introduces a novel approach to learn distance functions that maximizes the clustering of objects belonging to the ...
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We introduce an algorithm for mining expressive temporal relationships from complex data. Our algorithm, AprioriSetsAndSequences (ASAS), extends the Apriori algorithm to data sets in which a single data instance may c...
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Many systems attempt to forecast user navigation in the Internet through the use of past behavior, preferences and environmental factors. Most of these models overlook the possibility that users may have many diverse ...
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In this paper a discriminative manifold learning method for face recognition is proposed which achieved the discriminative embedding the high dimensional face data into a low dimensional hidden manifold. Unlike the re...
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ISBN:
(纸本)0769521282
In this paper a discriminative manifold learning method for face recognition is proposed which achieved the discriminative embedding the high dimensional face data into a low dimensional hidden manifold. Unlike the recently proposed LLE, Isomap and Eigenmap algorithms, which are based on reconstruction purpose, our method use the RCA algorithm to achieve nonlinear embedding and data discrimination at the same time. Also, the LLE and Isomap algorithms are crucially depends on the appropriateness of the neighborhood construction rule, in this paper a CK-nearest neighborhood rule is proposed to achieve better neighborhood construction. Experimental results indicate the promising performance of the proposed method.
MAP estimation of Gaussian mixtures through maximisation of penalised likelihoods was used to learn models of spatial context. this enabled prior beliefs about the scale, orientation and elongation of semantic regions...
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ISBN:
(纸本)0769521282
MAP estimation of Gaussian mixtures through maximisation of penalised likelihoods was used to learn models of spatial context. this enabled prior beliefs about the scale, orientation and elongation of semantic regions to be encoded, encouraging one-to-one correspondences between mixture components and these regions. In conjunction with minimum description lengththis enabled automatic learning of inactivity zones and entry zones from track data in a supportive home environment.
the support vector machine (SVM) is considered here in the context of pattern classification, the emphasis is on the soft margin classifier which uses regularization to handle non-separable learning samples. We presen...
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ISBN:
(纸本)0769521428
the support vector machine (SVM) is considered here in the context of pattern classification, the emphasis is on the soft margin classifier which uses regularization to handle non-separable learning samples. We present an SVM parameter estimation algorithm that first identifies a subset of,the learning samples that we call the support set and then determines not only the weights of the classifier but, also the hyperparameter that controls the influence of the regularizing penalty term, on basis thereof. We provide numerical results using several data sets from the public domain.
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