Titlebook: Machine Learning and Data Mining in Pattern Recognition; 5th International Co Petra Perner Conference proceedings 2007 Springer-Verlag Berl

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A Clustering Algorithm Based on Generalized Starsrforms previously defined methods and obtains a smaller number of clusters. Since the GStar algorithm is relatively simple to implement and is also efficient, we advocate its use for tasks that require clustering, such as information organization, browsing, topic tracking, and new topic detection.

Handedness

Hemoptysis

合同

舊式步槍

矛盾

An Empirical Comparison of Ideal and Empirical ROC-Based Reject Rulesn two different reject rules (the Chow’s and the ROC rule). In particular, the experiments show that the Chow’s rule is inappropriate when the estimates of the a posteriori probabilities are not reliable.

墊子

Outlier Detection with Kernel Density Functionseld a robust local density estimation. Outliers are then detected by comparing the local density of each point to the local density of its neighbors. Our experiments performed on several simulated data sets have demonstrated that the proposed approach can outperform two widely used outlier detection algorithms (LOF and LOCI).

拉開這車床

Critical Scale for Unsupervised Cluster Discoverythat the detected cluster, represented as a mode of the PDF, can be validated by observing the lifetime of the mode in scale space. Statistical properties of the lifetime, however, are unclear. In this paper, we propose a concept of the ‘critical scale’ and explore perspectives on handling it for the cluster validation.

chassis

Minimum Information Loss Cluster Analysis for Categorical Data. For this reason we use the latent class model only to define a set of “elementary” classes by estimating a mixture of a large number components. We propose a hierarchical “bottom up” cluster analysis based on unifying the elementary latent classes sequentially. The clustering procedure is controlled by minimum information loss criterion.

猛烈責罵

On Concentration of Discrete Distributions with Applications to Supervised Learning of Classifiersby showing explicitly that, depending on context, independent (’Na?ve’) classifiers can be as bad as tossing coins. Regardless of this, independence may beat the generating model in learning supervised classification and we explicitly provide one such scenario.