As a powerful tool for summarizing the distributed medical information, Meta-analysis has played an important role in medical research in the past decades. In this paper, a more general statistical model for meta-anal...
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ISBN:
(纸本)3540269231
As a powerful tool for summarizing the distributed medical information, Meta-analysis has played an important role in medical research in the past decades. In this paper, a more general statistical model for meta-analysis is proposed to integrate heterogeneous medical researches efficiently. the novel model, named mixture random effect model (MREM), is constructed by Gaussian Mixture Model (GMM) and unifies the existing fixed effect model and random effect model. the parameters of the proposed model are estimated by Markov Chain Monte Carlo (MCMC) method. Not only can MREM discover underlying structure and intrinsic heterogeneity of meta datasets, but also can imply reasonable subgroup division. these merits embody the significance of our methods for heterogeneity assessment. Both simulation results and experiments on real medical datasets demonstrate the performance of the proposed model.
Classification is one of the main tasks in machinelearning, datamining and patternrecognition. Compared withthe extensively studied data-driven approaches, the interactively user-driven approaches are less explore...
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ISBN:
(纸本)0780391365
Classification is one of the main tasks in machinelearning, datamining and patternrecognition. Compared withthe extensively studied data-driven approaches, the interactively user-driven approaches are less explored A granular computing model is suggested for re-examiningthe classification problems. An interactive classification method using the granule network is proposed, which allows multi-strategies for granule tree construction and enhances the understanding and interpretation of the classification process. this method is complementary to the existing classification methods.
Spatial datamining is a demanding field since huge amounts of spatial data have been collected in various applications, ranging form Remote Sensing to GIS, Computer Cartography, Environmental Assessment and Planning....
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ISBN:
(纸本)3540269231
Spatial datamining is a demanding field since huge amounts of spatial data have been collected in various applications, ranging form Remote Sensing to GIS, Computer Cartography, Environmental Assessment and Planning. Although there have been efforts for spatial association rule mining, but mostly researchers discuss only the positive spatial association rules;they have not considered the spatial negative association rules. Negative association rules are very useful in some spatial problems and are capable of extracting some useful and previously unknown hidden information. We have proposed a novel approach of mining spatial positive and negative association rules. the approach applies multiple level spatial mining methods to extract interesting patterns in spatial and/or non-spatial predicates. data and spatial predicates/association-ship are organized as set hierarchies to mine them level-by-level as required for multilevel spatial positive and negative association rules. A pruning strategy is used in our approach to efficiently reduce the search space. Further efficiency is gained by interestingness measure.
Assessing the similarity between objects is a prerequisite for many datamining techniques. this paper introduces a novel approach to learn distance functions that maximizes the clustering of objects belonging to the ...
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Human motion sequence-oriented spatio-temporal pattern analysis is a new problem in patternrecognition. this paper proposes an approach to human motion sequence recognition based on 2D spatio-temporal shape analysis,...
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ISBN:
(纸本)3540269231
Human motion sequence-oriented spatio-temporal pattern analysis is a new problem in patternrecognition. this paper proposes an approach to human motion sequence recognition based on 2D spatio-temporal shape analysis, which is used to identify diving actions. the approach consists of the following main steps. For each image sequence involving human in diving, a simple exemplar-based contour tracking approach is first used to obtain a 2D contour sequence, which is further converted to an associated temporal sequence of shape features. the shape features are the eigenspace-transformed shape contexts and the curvature information. then, the dissimilarity between two contour sequences is evaluated by fusing (1) the dissimilarity between the associated feature sequences, which is calculated by the Dynamic Time Warping (DTW), and (2) the difference between the pairwise global motion characteristics. Finally, sequence recognition is performed according to a minimum-distance criterion. Experimental results show that high correct recognition ratio can be achieved.
A general automatic method for clinical image segmentation is proposed. Tailored for the clinical environment, the proposed segmentation method consists of two stages: a learning stage and a clinical segmentation stag...
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ISBN:
(纸本)3540269231
A general automatic method for clinical image segmentation is proposed. Tailored for the clinical environment, the proposed segmentation method consists of two stages: a learning stage and a clinical segmentation stage. During the learning stage, manually chosen representative images are segmented using a variational level set method driven by a pathologically modelled energy functional. then a window-based feature extraction is applied to the segmented images. Principal component analysis (PCA) is applied to these extracted features and the results are used to train a support vector machine (SVM) classifier. During the clinical segmentation stage, the input clinical images are classified withthe trained SVM. By the proposed method, we take the strengths of bothmachinelearning and variational level set while limiting their weaknesses to achieve automatic and fast clinical segmentation. Both chest (thoracic) computed tomography (CT) scans (2D and 3D) and dental X-rays are used to test the proposed method. Promising results are demonstrated and analyzed. the proposed method can be used during preprocessing for automatic computer aided diagnosis.
Real life transaction data often miss some occurrences of items that are actually present. As a consequence some potentially interesting frequent patterns cannot be discovered, since with exact matching the number of ...
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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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machinelearning techniques are widely used in the analysis of biomedical datasets. Modern devices tend to produce voluminous, high-dimensional datasets for which medical practitioners require high-performance, user-f...
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Fast control chart patternrecognition aids in instantaneous detection of abnormal functioning of a system. In this paper, we present a parallel algorithm for fast control chart patternrecognition. It addresses three...
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