Concept lattice, core structure in Formal Concept Analysis has been used in various fields like software engineering and knowledge discovery. In this paper, we present the integration of Association rules and Classifi...
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ISBN:
(纸本)3540269231
Concept lattice, core structure in Formal Concept Analysis has been used in various fields like software engineering and knowledge discovery. In this paper, we present the integration of Association rules and Classification rules using Concept Lattice. this gives more accurate classifiers for Classification. the algorithm used is incremental in nature. Any increase in the number of classes, attributes or transactions does not require the access to the previous database. the incremental behavior is very useful in finding classification rules for real time data such as image processing. the algorithm requires just one database pass through the entire database. Individual classes can have different support threshold and pruning conditions such as criteria for noise and number of conditions in the classifier.
We propose an unsupervised, probabilistic method for learning visual feature hierarchies. Starting from local, low-level features computed at interest point locations, the method combines these primitives into high-le...
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ISBN:
(纸本)3540269231
We propose an unsupervised, probabilistic method for learning visual feature hierarchies. Starting from local, low-level features computed at interest point locations, the method combines these primitives into high-level abstractions. Our appearance-based learning method uses local statistical analysis between features and Expectation-Maximization to identify and code spatial correlations. Spatial correlation is asserted when two features tend to occur at the same relative position of each other. this learning scheme results in a graphical model that constitutes a probabilistic representation of a flexible visual feature hierarchy. For feature detection, evidence is propagated using Belief Propagation. Each message is represented by a Gaussian mixture where each component represents a possible location of the feature. In experiments, the proposed approach demonstrates efficient learning and robust detection of object models in the presence of clutter and occlusion and under view point changes.
In supervised machinelearning, the partitioning of the values (also called grouping) of a categorical attribute aims at constructing a new synthetic attribute which keeps the information of the initial attribute and ...
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ISBN:
(纸本)3540269231
In supervised machinelearning, the partitioning of the values (also called grouping) of a categorical attribute aims at constructing a new synthetic attribute which keeps the information of the initial attribute and reduces the number of its values. In case of very large number of values, the risk of overfilling the data increases sharply and building good groupings becomes difficult. In this paper, we propose two new grouping methods founded on a Bayesian approach, leading to Bayes optimal groupings. the first method exploits a standard schema for grouping models and the second one extends this schema by managing a "garbage" group dedicated to the least frequent values. Extensive comparative experiments demonstrate that the new grouping methods build high quality groupings in terms of predictive quality, robustness and small number of groups.
Ranked transformations should preserve a priori given ranked relations (order) between some feature vectors. Designing ranked models includes feature selection tasks. Components of feature vectors which are not import...
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ISBN:
(纸本)3540269231
Ranked transformations should preserve a priori given ranked relations (order) between some feature vectors. Designing ranked models includes feature selection tasks. Components of feature vectors which are not important for preserving the vectors order should be neglected. this way unimportant dimensions are greatly reduced in the feature space. It is particularly important in the case of "long" feature vectors, when a relatively small number of objects is represented in a high dimensional feature space, in the paper, we describe designing ranked models withthe feature selection which is based on the minimisation of convex and piecewise linear (CPL) functions.
We present CTC, a new approach to structural classification. It uses the predictive power of tree patterns correlating withthe class values, combining state-of-the-art tree mining with sophisticated pruning technique...
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ISBN:
(纸本)0769522785
We present CTC, a new approach to structural classification. It uses the predictive power of tree patterns correlating withthe class values, combining state-of-the-art tree mining with sophisticated pruning techniques to find the k most discriminative pattern in a dataset. In contrast to existing methods, CTC uses no heuristics and the only parameters to be chosen by the user are the maximum size of the rule set and a single, statistically well founded cut-off value. the experiments show that CTC classifiers achieve good accuracies while the induced models are smaller than those of existing approaches, facilitating comprehensibility.
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.
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.
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.
this paper proposes a learning approach for discovering the semantic structure of web pages. the task includes partitioning the text on a web page into information blocks and identifying their semantic categories. We ...
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ISBN:
(纸本)0769524206
this paper proposes a learning approach for discovering the semantic structure of web pages. the task includes partitioning the text on a web page into information blocks and identifying their semantic categories. We employed two machinelearning techniques, Adaboost and SVMs, to learn from a labeled web page corpus. We evaluated our approach on general web pages from the World Wide Web and obtained encouraging results. this work can be beneficial to a number of web-driven applications such as search engines, web-based question answering, web-based datamining as well as voice enabled web navigation.
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.
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