class label
Exploiting Class Label Frequencies for Text Classification
... global class label frequencies as a weighting scheme to rank all data points in the training ...each class x in the training set a local class label frequency Lx is computed according ...
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Fine Grained Class Label Markup of Search Queries
... explicit class-label extraction with a la- tent class ...This class-label correlation ( CLC ) model admits a robust parallel approxi- mation, allowing it to scale to large amounts of ...
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Class Label Enhancement via Related Instances
... model class- instance relations to propagate ...of class-label prop- ...of class-instance pairs to propagate the class-labels to the remaining unlabeled ...for class-label ...
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Contextual Classification of Image Patches with Latent Aspect Models
... In this paper, we address the problem of classifying image regions into semantic classes (see Figure 1) based on their associated patch number (throughout this paper, the term patch will mainly be used to denote an image ...
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Study of Pruning Techniques to Predict Efficient Business Decisions for a Shopping Mall
... III. ATTRIBUTE SELECTION MEASURES Attribute selection is the process of removing the redundant attributes that are deemed irrelevant to the data mining task. The objective of attribute selection is therefore to search ...
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Weakly Supervised Acquisition of Open Domain Classes and Class Attributes from Web Documents and Query Logs
... priate class labels to the sets from unstructured text. Various class labels are assigned to a total of 1,432 sets of ...the class labels is computed over a sample of instances, by manu- ally ...
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Detailed Analysis of Classification Techniques in Data Mining
... A classification technique is an organized approach for building classification model from the given input dataset. Some of the well-known classification techniques are Neural Networks, Rule-based Classifiers, K-Nearest ...
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A Simplified FDDL Algorithm for Face Recognition
... (class label information) and the Fisher discrimination message into the objective function for learning a structured discriminative dictionary, which is used for face ...
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A Conditional Random Field Approach to Unsupervised Texture Image Segmentation
... a class label depending on its own features, its neighbours’ features, and class ...optimal label is assigned the highest probability and the less promising labels get lower ...
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Multi-Label Latent Spaces with Semi-Supervised Deep Generative Models
... the class label – is this an image of an animal, human, car, plane, or truck? We believe that a model that is aware of camera elevation E and lighting L should be able to improve accuracy in predicting Y ...
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Online Full Text
... Subsequently, closed itemsets are exhaustively enumerated using LCM from the transaction database generated in the previous section. Fig. 2c shows the closed itemsets (and their rule representations ) extracted from the ...
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Classification Accuracy in Cognitive Load for Users Preference in Web based Learning
... In this paper, a questionnaire is used to identify the cognitive load of the student and his website layout preference in a web learning environment. The cognitive behavior of the user is captured through questionnaire. ...
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Extend Classical Decision Tree Building Algorithms to Handle Data with Uncertain Numerical Attributes using the Distribution-Based Approach
... a class label and being represented by a feature vector, the task is to algorithmically build a model that predicts the class label of an unseen test tuple based on the tuple’s feature ...
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Optimum Ensemble Classification for Fully Polarimetric SAR Data Using Global-Local Classification Approach
... In spite of the considerable amount of work that has been carried out on the use of an ensemble of classifiers in recent years; only a few applications have been reported for PolSAR data. As literature reported [5], a ...
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Ideal code constrained supervised sparse coding
... the class label ...the class labels of the training samples are ...the class label to conduct the sparse coding and codebook learning, so that the generated sparse code and codebook can ...
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Title: Comparison of Classifiers in Data Mining
... no class label), the classifier will predict that X belongs to the class having the highest posterior probability, conditioned on ...the class Ci if and only if : ...
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Model Transfer with Explicit Knowledge of the Relation between Class Definitions
... a class correspon- dence table described in Section ...the class label of each example on the support scheme into a set of candidate class labels on the target scheme via the class ...
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A multi-label approach to target prediction taking ligand promiscuity into account
... Classification approaches based upon Naïve Bayes markedly feature in the probabilistic classification models for target-fishing [10, 12, 13, 15, 19] (and refer- ences therein). For this reason, we concentrated on this ...
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Optimal Kernel Marginal Fisher Analysis for Face Recognition
... Marginal fisher analysis(MFA)[9] is a recently proposed manifold learning method for feature extraction and dimensionality reduction, it is based on the graph embedding framework and explicitly considers the local ...
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A Hybrid Approach for Gender Classification of Web Images
... For gender recognition purpose, we have used a technique which is a well known feature of DCT. In DCT, the whole test image is broken up into a regular grid of blocks which will not overlap each other. Every block is ...
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