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 128 for “"Nearest neighbors"”.
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Multi-scale Deep Nearest Neighbors
… nature. We propose Multi-scale Deep Nearest Neighbour (MsDNN) which is a differentiable loss function that aims to maximize the expected sample margin for every training sample. The output of MsDNN is an embedding space. We evaluate the resulting space from two angles. From the …
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Applying a randomized nearest neighbors algorithm to dimensionality reduction
In this thesis, I implemented a randomized nearest neighbors algorithm in order to optimize an existing dimensionality reduction algorithm. In implementation I resolved details that were not considered in the design stage, and optimized the nearest neighbor system for use by the dimensionality …
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Landmine classification using possibilistic K-nearest neighbors with wideband electromagnetic induction data
… REQUEST.] In this thesis, a possibilistic K-nearest neighbor classifier is presented to distinguish between and classify mine and non-mine targets on data obtained from wideband electromagnetic induction sensors. The goal of this work is to develop methods for classifying wide-band …
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KNN Optimization for Multi-Dimensional Data
<p>The K-Nearest Neighbors (KNN) algorithm is a simple but powerful technique used in the field of data analytics. It uses a distance metric to identify existing samples in a dataset which are similar to a new sample. The new sample can then be classified via a class majority voting of its most …
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An improved algorithm for iris classification by using support vector machine and binary random machine learning
… Sigmoid and Polynomial), Random Forest (RF), k-Nearest Neighbors(k-NN) and Random Nearest Neighbors (RNN) as a method. The first objective of this study is to improve a new algorithm technique for classification. The new algorithm come from a combination of an ideas of k-NN algorithm and …
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NBA Sleep Tracking Data Imputation
… four main techniques are evaluated: K-Nearest Neighbors Regression, Linear Interpolation, Linear Regression, and Quadratic Regression. Each technique is applied and evaluated on key sleep metrics such as sleep duration, rMSSD (Root Mean Square of the Successive Differences between …
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Atomistic computer simulation analysis of nanocrystalline nickel-tungsten alloys
… preferred to associate with each other as third-nearest neighbors, but at very high concentrations formations with W atoms as second nearest neighbors were also observed.
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Three Essays in Applied Econometrics: with Application to Natural Resource and Energy Markets
… the Nadraya-Watson estimator, and the k-Nearest Neighbors method developed by Hallin et al. (2004b), Lu and Chen (2002), P.M. Robinson (2011) and Li and Tran (2009). With data sampled on a rectangular grid in a nonlinear random field, the results show that nonparametric local linear …
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High Frequency Transmission Spectroscopy
… of the S21 scattering parameter with a k-nearest neighbors clustering approach achieved an accuracy of 88% when classifying devices based on the individual manufacturing lot. Similarly, a test setup for performing HFTS on liquid samples was developed. By applying both k-nearest neighbors …
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Design and Implementation of a Pivot Shift Prototype for Quantitative Analysis
… Two schemes (metric based classification and k nearest neighbors) have been applied to the data set to empirically learn and judge ACL diagnosis.'
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Nearest neighbor search for cyro-electron microscopy images
… part of the thesis, we try four different k-nearest neighbors search (KNNS) algorithms on cryo-EM projection images classifying them based on the viewing directions. Brute force search (BFS) could be very time consuming when the dataset is large. Therefore, strategies like locality sensitive …
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Brief Study of Classification Algorithms in Machine Learning
… commonly used Machine Learning algorithms: k-Nearest Neighbors (kNN), Decision Trees and Naïve Bayes. All these algorithms fall under the Classification algorithm category of Unsupervised Machine Learning. This paper is constructed structurally in explaining the working theory behind each …
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Scalable online nonlinear goal-oriented inference with physics-informed maps
This thesis develops a physics-informed k-nearest neighbors approach, which draws from both physics-based modeling and data-driven machine learning. In doing so, our method achieves robustness and increased accuracy with small datasets, while being cheap to apply. Our method tackles the challenges …
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Automatic fall risk detection based on imbalanced data
… After oversampling on our training data, the K-Nearest Neighbors (KNN) algorithm achieves the best performance. This experiment provides evidence that our approach is more interpretable, with key features from skeleton information, and workable in multi-people scenarios.
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Prediction Intervals: The Effects and Identification of Sparse Regions for Nonparametric Regression Methods
… using prediction intervals from quantile k-nearest neighbors and quantile random forest can be biased to low or too high from the nominal level under various situations of sparsity. We also introduce a test that can be used to see if a new data point lies in an area of sparse data so that …
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Apklausų dalyvių aktyvumo analizė, pritaikant įvairius binarinio klasifikavimo algoritmus /
… algorithms were selected: logistic regression, K-nearest neighbors, decision tree, support vector machine, and XGBoost classifiers. The best results were obtained using the XGBoost classifier, with oversampling applied to the training dataset. The best model achieved a sensitivity (recall) metric …
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A method for detecting nonequilibrium dynamics in active matter
… the KLD from a stationary time series using a k-nearest neighbors estimator, comparing the time-forwards process to the time-reversed process Using time series data of probe particles embedded in the actin cortex, we establish a lower bound for the entropy production of cortical activity. Our …
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Worst-case Performance of Popular Approximate Nearest Neighbor Search Implementations: Guarantees and Limitations
Graph-based approaches to nearest neighbor search are popular and powerful tools for handling large datasets in practice, but they have limited theoretical guarantees. We study the worst-case performance of recent graph-based approximate nearest neighbor search algorithms, such as HNSW, NSG and …
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Incremental learning algorithm for anomaly detection applied to computed tomography scans in nuclear industry
… as Naive Bayes or proximity measures such as nearest neighbors on numeric 1D datasets and for intrusion detection. In this research, a new soft thresholding-based algorithm that can enhance model prediction in existing IL frameworks and ensure stable training towards the desired prediction …
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Energy load prediction for Open Charge Point Protocol - compliant electric vehicle charging stations
… this platform, four models—Random Forest, K-Nearest Neighbors (KNN), Long Short-Term Memory (LSTM), and Prophet—are evaluated with expanding-window cross-validation on 30 stations and Adaptive Charging Network (ACN) datasets. Key findings show that data regularity and density strongly …
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