Classification and Prototype Selection
Instance classification with prototype selection
... given a query image, the goal is to predict the presence or absence of an object within a pre-determined set. We fo- cus on instance classification, i.e. we consider objects with small intra-class variations such ...
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Evaluation of Feature Subset Selection, Feature Weighting, and Prototype Selection for Biomedical Applications
... Table 7 lists the overall accuracies for the different approaches using the E. coli dataset. Naïve Bayesian shows the best overall accuracy while decision tree in- duction exhibits the worst one. The result for Naïve ...
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k-NN Boosting Prototype Learning for Object Classification
... Object classification is a challenging task in computer ...(𝑘-NN) classification rule, which has shown to be very effective when dealing with local image de- ...performing prototype selection ...
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Transfer learning for image classification with sparse prototype representations
... our prototype selection ...ture selection approach builds a joint sparse classifier on the feature space [11], or a random [11] or hidden [2] projection of that feature space, our method discovers a ...
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MRPR: a MapReduce solution for prototype reduction in big data classification
... Join, Filtering and Fusion; aiming to provide more accurate preprocessed sets. We have found that a reducer based on fusion of prototypes permits to obtain reduced sets with higher reduction rates and accuracy ...
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Distributed Fuzzy Rough Prototype Selection for Big Data Regression
... a prototype selection algorithm with a similar setting as ...of classification; implying that we deal with a real-valued rather than discrete ...
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On the selection of the globally optimal prototype subset for Nearest-Neighbor classification
... Saïd Business School, University of Oxford, Oxford OX1 1HP, United Kingdom, [email protected] T he nearest-neighbor classifier has been shown to be a powerful tool for multiclass classification. We ...
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On the selection of the globally optimal prototype subset for nearest-neighbor classification
... The average proportion of correctly classified objects in the testing sample, for different values of p and -, together with the slopes of the regression lines linking percentage of corre[r] ...
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Advanced methods for prototype-based classification
... We demonstrate the usefulness by means of matrix learning in GLVQ. Beside the desired effect on the convergence behavior, the technique turns out to be beneficial to prevent over-fitting effects and numerical ...
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Fuzzy variants of prototype based clustering and classification algorithms
... All of the subsequently presented measures are also available for fuzzy data. Further details and restrictions can be found in the respective sections. 2.4.1 Measures based on separation and compactness These measures ...
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Multiclass Classification with Multi-Prototype Support Vector Machines
... In Section 2 we give some preliminaries and the notation we adopt along the paper. Then, in Section 3 we derive a convex quadratic formulation for the easier problem of learning one prototype per class. The ...
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S ECOND PROTOTYPE. Activity: Lead Partner: Document classification:
... In order to provide for remote users high interactivity level, GVid software was implemented in VizLitG as video-streaming module. The most important GVid classes were renewed to support VTK 5.4. and enwrapped by Java. ...
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Adaptive Learning for Algorithm Selection in Classification
... and prototype systems with a variety of models and algorithms exist at the analyst’s ...the selection among them is left to the ...the classification, ...This selection is one of the most ...
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Distance Measures for Prototype Based Classification
... Abstract. The basic concepts of distance based classification are intro- duced in terms of clear-cut example systems. The classical k-Nearest- Neigbhor (kNN) classifier serves as the starting point of the ...
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Prototype-based classification by fuzzification of cases
... our prototype based ...of classification, for the aggregation of member- ship degrees or similarities, we used the mean operator because here we ignore dependencies and priorities between ...
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Combining dissimilarity measures for prototype-based classification
... Abstract. Prototype-based classification, identifying representatives of the data and suitable measures of dissimilarity, has been used successfully for tasks where interpretability of the ...
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Prototype based fuzzy classification in clinical proteomics
... with prototype methods so ...presented prototype based classifiers are applicable also in non-clinical domains but they show some proper- ties which make them very desirable in the context of clinical ...The ...
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Classification, selection and evolution
... In other words, members of a species have the same structural (morphological) and internal working mechanisms (physiological). If they reproduce sexually, they can only do so with members of the same species. Biologists ...
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Implementation of Prototype Based Credal Classification approach For Enhanced Classification of Incomplete Pattern
... ----------------------------------------------------------------****-------------------------------------------------------------- Abstract— Most of the time values are missing in database, which should be dealt with. ...
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Efficient Adaptation of Structure Metrics in Prototype-Based Classification
... In addition to an improved class separation in adapted distances, the learned scoring could highlight the importance of structural replacement operations, and thus give further insight into the classification ...
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