dissimilarity measure
A multi-labeled tree dissimilarity measure for comparing “clonal trees” of tumor progression
... the measure(s) used should be versatile enough to dis- criminate real topological differences between trees from those differences due to the type and coverage of the HTS data used by a method; ...
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A new dissimilarity measure for finding semantic structure in category fluency data with implications for understanding memory organization in schizophrenia
... inter-item dissimilarity using the raw inter-item distance data that can be obtained from fluency ...new dissimilarity measure, the mcf metric, that improves on that used in previous studies in ...
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A new dissimilarity measure for finding semantic structure in category fluency data with implications for understanding memory organization in schizophrenia
... within-list dissimilarity measure, certainly acts in the right directionÑits value is smallest (reflecting item similarity) for items that co-occur with high frequency and largest (reflecting ...
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An iterative scheme for feature-based positioning using a weighted dissimilarity measure
... weighted dissimilarity measure with the goal of reducing the impact of large errors among the measured or modeled ...the dissimilarity measure, which in turn quantifies the difference between ...
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A New Dissimilarity Measure between Feature Vectors
... a measure of the difference between direct and inverse RDW distance bring added certainty regarding the degree of similarity of two feature- vectors, its low values indicating a "similarity "greater of ...
6
Measures of precision for dissimilarity based multivariate analysis of ecological communities
... the dissimilarity measure used, so cannot be easily com- pared across ...chosen dissimilarity measure is able to ...larity measure should be ...
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keyword extraction from a single document using centrality measures PREMI2007
... problem in text mining. Keyword extraction techniques are beginning to be used in other applications such as web-page clustering and discovering emerging topics by analyzing co-citation graphs. In this paper, we proposed ...
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Towards the Automatic Identification of Adjectival Scales: Clustering Adjectives According to Meaning
... Finally, the fourth stage clusters the adjectives into groups according to the dissimilarity measure, so that adjectives with a high degree of pairwise similarity fall in the same cluste[r] ...
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Spatial analyses of wildlife contact networks
... graph dissimilarity measure is proposed to quantify how close an observed contact network is to being purely spatial whereby its edges are completely deter- mined by the spatial arrangement of its ...second ...
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RECOGNITION OF ARABIC CHARACTERS AND FONTS
... The principle of the suggested recognition method is based on the characterization of each character by a shape index. This index represents a set of parameters, invariant to translation, rotation and scale parameter. In ...
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A Generalized Kernel Approach to Dissimilarity-based Classification
... In summary, Sch¨ olkopf provides a new framework for distance based algorithms.The squared Euclidean distance can now be realized in another feature space by using a suitable kernel function, which should be ...
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International Journal of Computer Science and Mobile Computing
... The Euclidean distance means the dissimilarity measure. it specifies dissimilarities than similarities between the documents. Thus, it is not able to effectively capture the nonlinear manifold structure ...
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Identification and classification of top k string with weighted dissimilarity measures
... table-1, the correlation between the weighted dissimilarity measure and log linear is demonstrated. For the expression "search" the client mistyped the statement as "sarech", so the ...
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Survival Analysis of Hepatocellular Carcinoma
... discrepancy measure between combinations is obtained from the data ...This dissimilarity measure is used to obtain clusters of combinations with a hierarchical ...
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Research / LION OPTIMIZATION ALGORITHM USING DATA MINING CLASSIFICATION AND CLUSTERING MODELS
... the dissimilarity measure only uses the user-given parameter for adjusting the significance of ...the dissimilarity between data objects and prototypes of ...
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An Effective FCM Approach of Similarity and Dissimilarity Measures with -Cut
... and dissimilarity measures have been studied in this research ...and dissimilarity measures with FCM while handling the mixed ...cosine measure with m* value of ...
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Bayesian inference for the dissimilarity index in the presence of spatial autocorrelation
... The application that motivated this paper is religious segregation in Northern Ireland and the extent to which it has changed between 2001 and 2011, an issue that has received a great deal of attention recently (see for ...
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Characterization, dissimilarity and genetic parameters on cassava varieties
... Despite of having high yield potential, reaching in some regions yields above 80 t ha -1 (Albuquerque et al. 2008), the national cassava productivity is low (14.73 t ha -1 ) and has not evolved (MinasGerais 2010). ...
6
Measure For Measure
... Draw some sample fluid from your microtube Push the plunger knob down to the first stop.. Keeping the plunger down, lower the tip into the sample fluid just below the surface (~2 to 3m[r] ...
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Ricci flow embedding for rectifying non-Euclidean dissimilarity data
... Pairwise dissimilarity representations are frequently used as an alternative to feature vectors in pattern recognition. One of the problems encountered in the analysis of such data, is that the dissimilarities are ...
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