k-nearest neighbor learning
A Retrieval Matching Method Based Case Learning for 3D Model
... trieval results, but most of these systems only use the single method. The model features extracted in this way will be not highly robust and very sensitive to noise. It will result in unsatisfactory search results. ...
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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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Evaluation Of The Performance Of K-Nearest Neighbor Algorithm In Determining Student Learning Styles
... the K value were found to be correlated [1]with ...The K value was not strongly significant in determining the ...of k and small numbers of records the error is ...of K, the error ...the ...
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k-Expected Nearest Neighbor Search over Gaussian Objects
... In recent years, the advances in computing devices and technologies have been calling for researchers’ attention to develop novel solutions for emerging new problems. For instance, the location information obtained from ...
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A Deterministic K means Algorithm based on Nearest Neighbor Search
... using nearest neighbor search for computing suitable initial clusters centroids instead of random ones, then apply k-means procedure to refine the ...machine learning repository, in order to ...
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FML-kNN: scalable machine learning on Big Data using k-nearest neighbor joins
... Similarly to many data analysis and management tasks, kNN joins suffer from the curse of dimensionality [16]. Liao et al. [17] stated that as the number of dimensions increases, such techniques need an exponentially ...
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Title: PERFORMANCE ANALYSIS OF HYBRID APPROACH OF K-NN ALGORITHM USING MULTIPLE-LEVEL LEARNING FOR TEXT CLASSIFICATION
... The k-nearest-neighbor algorithm is a basic instance based learning method and widely used in similarity ...the k-NN classifiers find k nearest patterns in the training ...
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Generalized Mean Distance-based k Nearest Centroid Neighbor Classifier
... machine learning data sets repository (Bache & Lichman, 2013) is used in the experiments and the classification performance is measured using accuracy ...
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Pedagogy And Reduction Of K-nn Algorithm For Filtering Samples In The Breast Cancer Treatment
... Machine learning concepts and their specified tools in this diagnosing is bringing fruitful ...machine learning have helped most of the medical experts to draw conclusions easily compared to the traditional ...
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Exact fuzzy k Nearest neighbor classification for big datasets
... the most effective methods in supervised learning problems. It classifies unseen cases comparing their similarity with the training data. Nevertheless, it gives to each labeled sample the same importance to ...
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Statistical Data Classification Using Instance Based Learning Algorithm
... scenario. K-NN Algorithm and Instance based learning is greatly helps in classification of data on grounds of ...in K-Nearest Neighbor is evolved on the necessity to distinct analysis ...
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Machine Learning Based Technique for Detection of Rank Attack in RPL based Internet of Things Networks
... Abstract: Internet of Things (IoT) is a new Paradiagram in the network technology. It has the vast application in almost every field like retail, industries, and healthcare etc. It has challenges like security and ...
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Hand written character recognition using SVM
... Supervised learning uses knowledge of labels for instances used for building the model while instances for unsupervised learning are ...it, k- nearest neighbor (KNN) and neural ...
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Secure K-Nearest Neighbor Search By Keywords
... profound learning plot that can surmise the conceivable sicknesses given the inquiries of wellbeing ...shallow learning approaches, which are typically prepared on healing facility created persistent ...
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Evaluating the Performance of Dual Weighted K Nearest Neighbor Classifier
... Dynamic K-Nearest Neighbors Naive Bayes with Attribute Weighted method to improve KNN's accuracy was developed by ...Eager learning and Lazy learning are combined together to improve the ...
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A Study of Current State of Work done for Classification in Indian Languages
... Harikrishna D M, K. Sreenivasa Rao (2015) [3] worked to classify 450 hindi and telugu short stories into Fable, Folk-tale, Legend genre. Pre-processing involves lemmatization (stemming) Stop-words removal. Feature ...
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Survey on Classification and Prediction Approaches in Traffic Flow
... Yuqi Wang and Wengen Li proposed method for predicting traffic congestion correlation between road segments on GPS trajectories [22]. Method extract various features on each pair of road segments from road network. Result ...
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Spatio-Temporal Query Processing on Weighted Timestamp Data Environment
... To our knowledge, currently there is a very narrow selection of solutions for the DTop-k query, and no previous work on the DkNN query. The only existing solutions for DTop-k (reviewed in Section 2) employ ...
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Implementation Of An Efficient Hybrid Classification Model For Heart Disease Prediction
... SVM is supervised learning process of classifying data into labels. The dataset is first used to train SVM about classes and after that SVM is capable of classifying new data. SVM is centered on numerical ...
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REDUCTION IN FALSE POSITIVE RATE BY COMBINING SVM AND KNN ALGO
... active learning support vector machine process is introduced at this stage; support vector machine (SVM) is used to perform support vector machine training rely on various training data ...active learning ...
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