Measures of class separability can provide valuable insights into data, and suggest promising classification algorithms and approaches in data mining. We compare three simple class separability measures used in supervised machine learning. Their relative effectiveness is evaluated through their functional relationships and their random projections of data onto R for visualization. We conclude t...

we present the basic results on the representation theory of the alternating groups. our approach is based on clifford theory.

We investigate the separability of arbitrary n-dimensional multipartite identical bosonic systems. An explicit relation between the dimension and the separability is presented. In particular, for n = 3, it is shown that the property of PPT (positive partial transpose) and the separability are equivalent for tripartite systems.

Geometric separability is a generalisation of linear separability, familiar to many from Minsky and Papert’s analysis of the Perceptron learning method. The concept forms a novel dimension along which to conceptualise learning methods. The present paper shows how geometric separability can be defined and demonstrates that it accurately predicts the performance of a at least one empirical learni...

We establish new conjugacy criteria for half-linear second order differential equations. These criteria are based on the relationship between conjugacy of the investigated equation and nonpositivity of the associated energy functional.

The separability of a resistor-inductorcapacitor-mutual inductance (RLCM) network is defined in this paper. Some separability, reducibility and controllability (observability) criteria of RLCM networks over ( ) F z are derived. Two illustrative examples are given. Index Term—RLCM Networks, Separability, Reducibility, Controllability