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Oct 15, 2023 · In machine learning, researchers can measure the distance between samples by introducing different distance metrics, such as K -Means clustering and k -nearest ...
May 11, 2024 · This paper introduces a novel supervised classification method based on dynamic clustering (DC) and K-nearest neighbor (KNN) learning algorithms, ...
Apr 23, 2024 · This method uses a quantum circuit to calculate the. Hamming distance between testing data and training data, and sets a threshold to obtain K nearest neighbors ...
Feb 20, 2024 · In regression tasks, KNN predicts the output for a new data point by averaging the values of the K nearest neighbors. This method is based on the assumption ...
Aug 21, 2024 · We investigate an open question in the study of the curse of dimensionality: Is it possible to find the single nearest neighbor of a query in high dimensions?
Nov 13, 2023 · In this paper, we propose and evaluate the idea of exploiting a quantum locality technique to reduce the size and improve the performance of QML models.
Jul 11, 2024 · This study presents a novel kNN learning method based on a graph neural network, named kNNGNN. Given training data, the method learns a task-specific kNN rule ...
May 20, 2024 · We compared MLKR with four standard regression algorithms: linear regres- sion, k-nearest neighbor regression with cross vali- dation for parameter setting ...
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Feb 5, 2024 · Abstract. In this paper, we introduce a kNN-based regression method that synergizes the scal- ability and adaptability of traditional non-parametric kNN ...
Aug 20, 2024 · The k-nearest neighbor (KNN) algorithm discriminates by the spatial distance of the samples, which is based on the strength of similarity of the spatial ...