The standard kNN algorithm suffers from two major drawbacks: sensitivity to the parameter value k, i.e., the number of neighbors, and the use of k as a global constant that is ind...
Background: As a variety of functional genomic and proteomic techniques become available, there is an increasing need for functional analysis methodologies that integrate heteroge...
The k-nearest-neighbor rule is one of the most attractive pattern classification algorithms. In practice, the choice of k is determined by the cross-validation method. In this wor...
Guided by an initial idea of building a complex (non linear) decision surface with maximal local margin in input space, we give a possible geometrical intuition as to why K-Neares...
Background: Various statistical and machine learning methods have been successfully applied to the classification of DNA microarray data. Simple instance-based classifiers such as...