k-Nearest Neighbour Classifiers

By Sarah Jane Delany. 2007

702 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/60f389f056ae250d896eb80b414a933537d51d6c

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Citations in the corpus

k-Nearest Neighbour Classifiers - A Tutorial (2020, background): “This paper is the second edition of a paper previously published as a technical report [14].”
Selecting critical features for data classification based on machine learning methods (2020, background): “Moreover, these experiments perform KNN, -tested with k = 5, 7 , and 9, which resampling using cross-validation tenfold.”
Diabetes Prediction Using Ensembling of Different Machine Learning Classifiers (2020, methodology): “Different ML models such as k-NN [31], DT, AB, RF, NB, and XB [32] have been trained (see Appendix D, Appendix E, Appendix F, Appendix G, Appendix H, and Appendix I respectively) and tested in the proposed framework.”
Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, unknown): “The mathematical algorithms for discrete classification include linear discriminant analysis (LDA) (259, 288, 629), support vector machine (56, 288, 780), ANNs (359), multilayer perceptron (43, 75), hidden Markov models (603, 655), k nearest neighbors classifier (177), and nonlinear Bayesian classif”
Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference (2020, methodology): “The typical methodology is to embed each data example into a vector space and (1) use an off-the-shelf distance metric to perform a similarity search (Cunningham and Delany, 2007) or (2) learn a distant metric between the embeddings (Sung et al., 2018).”
Actively Semi-Supervised Deep Rule-based Classifier Applied to Adverse Driving Scenarios (2019, background): “The Deep Rule-Based, K-nearest Neighbors (KNN) [28], Naive Bayes (NB) [29], Support Vector Machine (SVM) [30], and the deep convolutional neural network VGG–VD–16 classifiers are compared in terms of accuracy and processing time for labeled images.”
Computer-Aided Diagnostic System for Early Detection of Acute Renal Transplant Rejection Using Diffusion-Weighted MRI (2019, unknown): “ture [36]–[39] have been used in the CAD systems for organ transplantation prediction using clinical and demographic data of patients, including (but not limited to) artificial neu-”