Multi-hypothesis nearest-neighbor classifier based on class-conditional weighted distance metric

Fiber bundle over the starting points The special role that plays the starting point in the metric G induces another formal fiber bundle structure, where the base space is the

We cannot compute the normalized information distance, which is expressed in terms of the noncomputable Kolmogorov complexities of the objects concerned. Instead, we look at whether

The capabilities of the proposed NN classification method based on the PW distance metric and Dempster-Shafer theory (denoted as PNN-DST) was empirically assessed through two

While the main goal of the present paper is to analyti- cally establish the statistical properties of ensemble classifiers within the proposed framework of hypothesis testing, it

These methods aim to learn a metric that brings pairs of similar points closer together and pushes pairs of dissimilar points further away from each other.. Such supervision is

A phylogenetic analysis of representative 1CysPrx and 2CysPrx sequences coming from Viridiplantae has been performed to illustrate the potential source of information available in

The predictive model M defines a conditional probability distribution of target vari- ables given the value of context variables: p(A|C) and it is induced (learned) from examples in

Finally, the package implements one triplet learner and one quadruplet learner: Sparse Compositional Metric Learning (SCML, Shi et al., 2014) and Metric Learning from