k-nearest-neighbors classifier
Fast and Scalable Approaches to Accelerate the Fuzzy k Nearest Neighbors Classifier for Big Data
... Abstract—One of the best-known and most effective methods in supervised classification is the k nearest neighbors algorithm (kNN). Several approaches have been proposed to improve its accuracy, where ...
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kNN-IS: an iterative spark-based design of the k-nearest neighbors classifier for big data
... the k nearest neighbors together with their computed distance values will be emitted to the reduce ...final k nearest neighbors from the list provided by the ...
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Optimal functional supervised classification with separation condition
... plug-in classifier and derive a family of upper bounds on its worst-case excess risk over Sobolev ...popular k-nearest neighbors classifier is far from optimality in this specific ...
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Interpreting Neural Networks with Nearest Neighbors
... Deep k-Nearest Neighbors (D K NN) (Papernot and McDaniel, 2018) to neural models for text ...softmax classifier, but using the labels of the train- ing examples whose representations ...
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Evaluating the Performance of Dual Weighted K Nearest Neighbor Classifier
... such neighbors that are far away from the new instance had been presented by Schliep Hechenbichler ...Dynamic K-Nearest Neighbors Naive Bayes with Attribute Weighted method to improve KNN's ...
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From Linear Classifier to Convolutional Neural Network for Hand Pose Recognition
... (K-nearest neighbors), Artificial Neural Networks, Support Vector Machines (SVM), Finite State Machines, table-based classifiers, and expectation maximization over Gaussian mixture ...
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Opinion Mining and Topic Categorization with Novel Term Weighting
... niques. K-nearest neighbors algorithms, Bayes classifier, Rocchio classifier, support vector machine trained by COBRA and Neural Net- work have been applied as classification algo- ...
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Pattern Recognition Using K-Nearest Neighbors (KNN) Technique
... During early phase of this project, literature research has to be made in order to gather the information about the current classifier system and their weaknesses. Initially, the author will use alphabets as ...
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Convergence of Multi-pass Large Margin Nearest Neighbor Metric Learning
... margin nearest neighbor classification (LMNN) is a popular technique to learn a metric that improves the accuracy of a simple k- nearest neighbor classifier via a convex optimization ...the ...
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Pruned fuzzy K nearest neighbor classifier for beat classification
... Simple K-Nearest Neighbor (SKNN) classifier used in our previous work offers many advantages over other classifiers including simplicity and ease of parallel implementation, adaptability and online ...
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A modified k-nearest neighbor classifier to deal with unbalanced classes
... of neighbors, k, approach infinity such that k/N o 0, the error rate of the kNN classifier approaches the optimal Bayes error rate (Cover and Hart, ...kNN classifier can provide good ...
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Improving Convolutional Neural Networks’ Accuracy in Noisy Environments Using k-Nearest Neighbors
... The addition of a kNN stage slightly increases the computational cost, but given the success of recent fast kNN methods [32–34], it might be more efficient than increasing the number of parameters of the CNN in order to ...
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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 ...and k local mean vector of testing samples of each ...
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Detection Model for Daniel of Service Attacks using Random Forest and k Nearest Neighbors
... In this paper we propose and implement a hybrid classifier based on Random Forests (RDF) and k-Nearest Neighbor (KNN) algorithm for the classification of DoS attacks in a network. Random Forests is ...
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Compressed kNN: K-Nearest Neighbors with Data Compression
... 3. Rai, P.; Singh, S. A Survey of Clustering Techniques. Int. J. Comput. Appl. 2010, 7, 1–5. [CrossRef] 4. Bruni, R. Discrete models for data imputation. Discret. Appl. Math. 2004, 144, 59–69. [CrossRef] 5. Duan, Z.; ...
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Title: WHO ARE the LIKELIEST SURVIVORS of BREAST CANCER- An ANALYSIS by K NEAREST NEIGHBORS ALGORITHM
... base classifier is prepared on training data. Then a second classifier is used to create new models that target the observation in the training data that first classifier got ...
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A Completion on Fruit Recognition System Using K Nearest Neighbors Algorithm
... The k-nearest neighbor algorithm is the methodology that has been used to develop the fruit recognition ...the nearest examples to unknown fruit. KNN classifier classify to fruit mean color ...
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MACHINE LEARNING APPLICATION IN LOAN DEFAULT PREDICTION
... This paper proposes the use of statistical methods especially machine learning techniques to model and predict bank losses. We have used different machine learning algorithms specifically designed to handle ...
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Machine Learning Approach for Bottom 40 Percent Households (B40) Poverty Classification
... and k-Nearest Neighbors algorithm for classifying the B40 ...Each classifier is then optimized using different tuning parameter with 10-Fold Cross Validation for achieving the optimal values ...
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Automatic Thematic Classification of the Titles of the Seimas Votes
... text classifier for political texts (topics of parliamentary votes) in ...and k nearest neighbors (k-NN); (3) To compare the efficiency of the selected text classifiers when using ...
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