Distance metrics for α-shape based classifier
Improving nearest neighbor classifier using tabu search and ensemble distance metrics
... others for different data sets. The proposed ensemble fea- ture selection (FS) technique using TS/NN has achieved higher accuracy in all data sets except Diabetes. For Aus- tralian, German and Ionosphere data sets there ...
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Fuzzy Hyperline Segment Neural Network Pattern Classifier with Different Distance Metrics
... the distance metrics have different performance in terms of recognition rate, training time and testing ...same classifier, but with different distance metric. Distance metric changes ...
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Shape-Based Quality Metrics for Large Graph Visualization
... • The experiments use the notions of “untangledness” and “preference” as proxies for ground truth quality. It would be useful to test the metrics in a task-oriented experiment. Designing experiments to fully ...
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A Study of Distance Metrics in Histogram Based Image Retrieval
... various distance metrics on different color spaces. Euclidean distance, Manhattan distance, Histogram Intersection and Vector Cosine Angle distances are used to compare histograms in both RGB ...
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Generalized Mean Distance-based k Nearest Centroid Neighbor Classifier
... kNN classifier that assume nearest neighbors have equal importance with identical ...strategy classifier has been introduced (Zeng ...the distance is measured between test sample and k local mean ...
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Sobolev metrics on shape space of surfaces
... a shape can be modeled as an un-parameterized immersed sub-manifold, which is the notion of shape used ...Endowing shape space with a Riemannian metric opens up the world of Riemannian differential ...
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Exploiting diverse distance metrics for surrogate based optimisation of ordering problems
... RBFs based on observed ...RBF based on the higher-cost sample is large and positive, and the other is ...the distance, this means that at some stage the prediction surface becomes ...
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Implementation of Shape Descriptor Based On Distance Interior Ratio for Image Retrieval
... histogram, Shape Matching, Bounding Box, Shape Recognition ...using shape features has grabbed the attention. Shape is an important feature used for describing image ...many shape ...
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Mathematical techniques for shape modelling in computer graphics: A distance based approach
... Another series of experiments was also conducted on the surface definition of Eq. 5.9. This definition expresses the problem of the Voronoi tessellation, but also allow us to enhance it considerably. One such example of ...
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Binary classifier metrics for optimizing HEP event selection
... binary classifier evaluation The training and evaluation of binary classifiers in a given domain are often performed using solutions initially proposed in another domain to solve very different ...and ...
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Identifying Thresholds for Distance Design-based Direct Class Cohesion (D3C2) Metrics
... In the process of analyzing a class, an expert view of some things. In addition to the same parameter types with attribute types, experts also see from the naming attributes and methods. Naming similarity or similarity ...
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CiteSeerX — A Boosted Classifier Tree for Hand Shape Detection
... a distance metric based upon shape ...valid shape as belong to one of the predetermined clusters exemplified by an indicative hand ...hand shape it is equally applicable to other ...
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Euclidean-distance-based canonical forms for non-rigid 3D shape retrieval
... but based on local maxima and minima of geodesic distances to the two most geodesic distant vertices [34] ...for shape retrieval against classical MDS with all-pairs geodesic distances, but not using least ...
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Enhanced K Classifier A Framework to Measure the Reusability Metrics of Software
... CBSE- based software development, integration and ...various metrics proposed by Researchers during last few years to evaluate CBSS ...these metrics can be complex. For example, some of the ...
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Stanford: Probabilistic Edit Distance Metrics for STS
... aligned based upon structural overlap and lexical semantic similarity using a variety of word similarity metrics based on WordNet, vector space distributional sim- ilarity as calculated by InfoMap, ...
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Random Projection and Geometrization of String Distance Metrics
... In this paper, such combinations of “letter- pixels” were, mutatis mutandi, called “fragments”. Our method departs from an idea that one can, and should, associate random vectors to such fragments. But the idea can go ...
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K means with Three different Distance Metrics
... groups. Distance metrics plays a very important role in the clustering ...squared distance in each ...clusters based on K current centroids, and c) updating K centroids based on newly ...
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Performance Evaluation of Distance Metrics in the Clustering Algorithms
... search based clustering (M HSC ) technique. Here cluster center based encoding scheme is ...centers based on minimum Euclidean distance criterion and cluster centers repre- sented by the ...
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Single class classifier using FMCD based non-metric distance for timber defect detection
... features based on the orientation independent Grey Level Dependence Matrix (GLDM) [29] were then extracted from each region to form a feature vector representing the regions for the whole ...detected based ...
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Performance Evaluation of Face Recognition based on Multiple Feature Descriptors using Euclidean Distance Classifier
... T he personal identification is an actively growing area of research. The traditional personal authentication measures such as cards and passwords may not be enough for secure authentication of human identity. Biometric ...
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