Once the distance matrix is computed, the algorithm selects the K number of training samples that are the closest to the test sample to predict the test sample's result based on the set's majority(classification) or average (regression) value. Prediction Time Complexity O(k * n * d) to compute the distance between each test sample with every training sample to construct the distance matrix where: k = number of nearest neighbors selected n = size of the training set d = number of dimensions being used for the data This classification focused model predicts the label of the target based on the distance matrix between the target and each of the training samples to determine the K-number of sample that are the closest/nearest to the target. Computer Vision A distance matrix can be used in Neural Networks for 2D-to3D regression in image predicting machine learning models. Information Retrieval Distance Matricies Using Gaussian Mixture distance * Gaussian mixture distance for performing accurate nearest neighbor search for information retrieval. Under an established Gaussian finite mixture model for the distribution of the data in the database, the Gaussian mixture distance is formulated based on minimizing the Kullback-Leibler divergence between the distribution of the retrieval data and the data in database. The comparison the performance of the Gaussian mixture distance with the well-known Euclidean and Mahalanobis distance based on a precision performance measurement. Experimental results demonstrate that the Gaussian mixture distance function is superior in the others for different types of testing data. Potential basic algorithms worth noting on the topic of information retrieval is Fish School Search algorithm an information retival that partakes in the act of using distance matricies in order for gathering collective behavior of fish schools. By using a feeding operator to update their weights Eq. A: Eq. B: Stepvol defines the size of the maximum volume displacement preformed with the distance matrix. Specifically using a Euclidean distance Matrix Evaluation of the similarity or dissimilarity of Cosine similarity and Distance matrices * While the Cosine similarity measure is perhaps the most frequently applied proximity measure in information retrieval by measuring the angles between documents in the search space on the base of the cosine. Euclidean distance is invariant to mean-correction. The sampling distribution of a mean is generated by repeated sampling from the same population and recording of the sample means obtained. This forms a distribution of different means, and this