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The K-nearest neighbor classification performance can often be significantly improved through metric learning. Popular algorithms are neighbourhood components analysis and large margin nearest neighbor. Supervised metric learning algorithms use the label information to learn a new metric or pseudo-metric.
The kNN query is one of the hardest problems on multi-dimensional data, especially when the dimensionality of the data is high. The iDistance is designed to process kNN queries in high-dimensional spaces efficiently and it is especially good for skewed data distributions, which usually occur in real-life data sets. The iDistance can be ...
Structured k-nearest neighbours (SkNN) [1] [2] [3] is a machine learning algorithm that generalizes k-nearest neighbors (k-NN). k-NN supports binary classification, multiclass classification, and regression, [4] whereas SkNN allows training of a classifier for general structured output.
The k-nearest neighbor rule assumes a training data set of labeled instances (i.e. the classes are known). It classifies a new data instance with the class obtained from the majority vote of the k closest (labeled) training instances. Closeness is measured with a pre-defined metric. Large margin nearest neighbors is an algorithm that learns ...
Using MLE, we call the probability of the observed data for a given set of model parameter values (e.g., a pdf and a matrix ) the likelihood of the model parameter values given the observed data. We define a likelihood function L ( W ) {\displaystyle \mathbf {L(W)} } of W {\displaystyle \mathbf {W} } :
Partial least squares (PLS) regression is a statistical method that bears some relation to principal components regression and is a reduced rank regression; [1] instead of finding hyperplanes of maximum variance between the response and independent variables, it finds a linear regression model by projecting the predicted variables and the observable variables to a new space of maximum ...
Basic idea of LOF: comparing the local density of a point with the densities of its neighbors. A has a much lower density than its neighbors. The local outlier factor is based on a concept of a local density, where locality is given by k nearest neighbors, whose distance is used to estimate the density.
Optimization can help with fitting a model to data, where the goal is to identify the model parameters that minimize the difference between simulated and experimental data. Common parameter estimation problems that are solved with Optimization Toolbox include estimating material parameters and estimating coefficients of ordinary differential ...