Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 34 for “"K-Nearest Neighbour"”.
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Modern k-Nearest Neighbour Methods in Entropy Estimation, Independence Testing and Classification
Nearest neighbour methods are a classical approach in nonparametric statistics. The k-nearest neighbour classifier can be traced back to the seminal work of Fix and Hodges (1951) and they also enjoy popularity in many other problems including density estimation and regression. In this thesis we …
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Improved personalised data modelling using parameter independent fuzzy weighted k-nearest neighbour for spatio/spectro-temporal data
… exploration of the architecture, the weighted k-nearest neighbours algorithm used for the classification module is found to be prone to misclassification as it relies solely on the majority voting rule to determine the class for new data vector. Additionally, it does not consider the …
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Application of a K-Nearest Neighbour weather generator for simulation of historical and future climate variables in the Upper Thames River basin
… and future climate data using a nonparametric K-Nearest Neighbour block resampling weather generator with perturbation is presented (KnnCAD Version 4). The proposed approach is illustrated using a case study of the Upper Thames River basin in Ontario, Canada. KnnCAD V4 is shown to effectively …
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Tissue classification from electric impedance spectroscopy for haptic feedback in minimally invasive surgery
… were classified with least square error, k-Nearest Neighbour and Naïve Bayes using the measured electric impedance and the extracted model parameter values. The thesis culminates in applications of using EIS as part of implementing vibrotactile and force feedback applications involving sets …
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New approaches to modern statistical classification problems
… performance. Chapter 3 focuses on the k-nearest neighbour classifier. We first derive a new global asymptotic expansion for its excess risk, which elucidates conditions under which the dominant contribution to the risk comes from the locus of points at which each class label is equally …
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Hand and elbow gesture recognition based on electromyography signal
… of the features. Nevertheless, the K-Nearest Neighbour (KNN) classifier is used in order to classify the EMG signals for hand gestures. Lastly, the results from this project showed that the classifier has classified the gestures with a low performance due to the fewer amounts of the …
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Contributions for Handling Big Data Heterogeneity. Using Intuitionistic Fuzzy Set Theory and Similarity Measures for Classifying Heterogeneous Data
… The thesis also presents modification of the k-Nearest Neighbour classifier, called k-Nearest Neighbour Weighted Average (k-NNWA), to classify the heterogeneous dataset described by real-valued, crisp and linguistic data. Finally, the thesis also introduces a novel classification model, called …
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Condition Classification in Underground Pipes Based on Acoustical Characteristics. Acoustical characteristics are used to classify the structural and operational conditions in underground pipes with advanced signal classification methods
… spectrum for each condition/pipe defect. A K-nearest neighbour classifier (KNN) and Support vector machines (SVMs) classifier have been adopted to train the classification system to identify sediment and pipe surface defects by comparing the measured acoustic signals with a database containing …
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Machine Learning Classification of Gas Chromatography Data
… are analyzed and compared, including K-Nearest Neighbour (KNN), Support Vector Machines (SVM), Convolutional Neural Network (CNN), Decision Tree, and Random Forest (RF). Feature Selection is tested by applying window sweeps with the KNN algorithm. Feature Engineering is applied via the …
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KNN Query Processing in Wireless Sensor and Robot Networks
… event information to one of the robots. In the k nearest neighbour query processing problem in WSRNs, the robot receives event report needs to find exact k nearest robots (KNN) to react to the event, among those connected to it. We are interested in localized solutions, which avoid message …
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A PREDICTIVE MODEL FOR DIABETES USING MACHINE LEARNING TECHNIQUES (A CASE STUDYOF SOME SELECTED HOSPITALS IN KADUNA METROPOLIS)
… algorithms like Decision tree algorithm, K- Nearest Neighbour algorithm and Artificial Neural Networks on a dataset gotten from 44 Army Reference Hospital and Yusuf Danstoho Memorial Hospital Kaduna which constitutes of nine (9) attributes that was considered. The results indicated that ANN …
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Virtual forced splitting in multidimensional access methods
… together with new algorithms for region and K Nearest Neighbour search. We further report experimental results on construction, exact-match search and K-NN search of BV-trees, and show how they compare, very favourably, with the corresponding operations on the currently most popular …
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The comparative study of model-based and appearance based gait recognition for leave bag behind
… which is Support Vector Machine (SVM) and KNN (K nearest Neighbour) based on accuracy and misclassification rates (MER) metrics. The experiment results show that the accuracy and misclassification rate (MER) of Appearance-based approaches obtained is 93.66% and 6.33% respectively tested on SVM …
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Pruning methods for rule induction
… of this instance reduction method for k nearest neighbour algorithms in term of predictive accuracy and amount of reduction. Then we compared it with other instance reduction methods.We show that pruning classification rules with instance-reduction methods lead to a statistically …
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Discriminant analysis : a review of its application to the classificationof grape cultivars
… Non-parametrical approaches such as the K-nearest neighbour technique, the kernel method and ranking are briefly discussed. Chapter 3 deals with estimating the probability of misclassification. In Chapter 4 variable selection techniques are discussed. Chapter 5 briefly deals with sequential …
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Word based off-line handwritten Arabic classification and recognition. Design of automatic recognition system for large vocabulary offline handwritten Arabic words using machine learning approaches.
… are used for classification such as K nearest neighbour classifier (k-NN), neural network classifier (NN), Hidden Markov models (HMMs), and the Dynamic Bayesian Network (DBN). To test this concept, the particular pattern recognition problem studied is the classification of 32492 words …
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An enhanced Bayesian Network prediction model for football matches based on player performance
… other machine learning techniques such as k-Nearest Neighbour (kNN) and Decision Tree (DT) in order to compare with BN for prediction accuracy. The experimental results showed two fold; the proposed PP model using BN achieved a higher accuracy in predicting the outcomes for football matches …
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Classification of Galvanneal Steel using Optical Texture Analysis
… recognition techniques, genetic algorithm and K-nearest neighbour classifiers (Knn) and the rank-conditioned (R-C) morphological transform. Two novel techniques have emerged from the investigative research, a grey level co occurrence image filtration procedure and a morphological template …
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Word based off-line handwritten Arabic classification and recognition. Design of automatic recognition system for large vocabulary offline handwritten Arabic words using machine learning approaches.
… are used for classification such as K nearest neighbour classifier (k-NN), neural network classifier (NN), Hidden Markov models (HMMs), and the Dynamic Bayesian Network (DBN). To test this concept, the particular pattern recognition problem studied is the classification of 32492 words …
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Machine Learning Approaches and Web-Based System to the Application of Disease Modifying Therapy for Sickle Cell
… models, the random forest classifier, and the K-nearest neighbour classifier. In this aspect, combining the levenberg-marquardt algorithm, the voted perceptron classifier, the radial basis neural classifier, and random forest classifier obtain the highest rate of performance and accuracy. This …
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