ABSTRACT. We present a novel nearest neighbor rule-based implementation of the structural risk minimization principle to address a generic clas-.

We present a novel nearest neighbor rule-based implementation of the structural risk minimization principle to address a generic classification problem.

We present a novel nearest neighbor rule-based implementation of the structural risk minimization principle to address a genetic clas- sification problem.

In this paper, we present a novel nearest neighbor rule-based implementation of the structural risk minimization principle to address a generic ...

We present a novel nearest neighbor rule-based implementation of the structural risk minimization principle to address a generic classification problem.

Extending the objective function to incorporate a regularizer leads to Structural. Risk Minimization (srm). This provides us with a unified view on many machine ...

In this paper, we present a technique that combines these two classifiers by adopting a NN rule-based structural risk minimization classifier. Using synthetic ...

Advanced algorithms for signal processing simultaneously account for nonlinearity, nonstationarity, and non-Gaussianity. The article examines the use of ...

Support vector machines (SVMs) are by far the most sophisticated and powerful classifiers available today. How- ever, this robustness and novelty in ...

One-class classification is an important problem with applications in several different areas such as outlier detection and machine monitoring.