We tackle the problem of separating two finite sets of samples by means of a spherical
surface, focusing on the case where the center of the sphere is fixed. Such approach …

We consider a multiple instance learning problem where the objective is the binary
classifications of bags of instances, instead of single ones. We adopt spherical separation as …

We face the problem of strictly separating two sets of points by means of a sphere,
considering the two cases where the center of the sphere is fixed or free, respectively. In …

We introduce a method for edge detection which is based on clustering the pixels
representing any given digital image into two sets (the edge pixels and the non-edge ones) …

We propose two different approaches for spherical separation of two sets. Both methods are
based on minimizing appropriate nonconvex nondifferentiable error functions, which can be …

Abstract The semiproximal Support Vector Machine technique is a recent approach for
Multiple Instance Learning (MIL) problems. It exploits the benefits exhibited in the …

We present an application of a Multiple Instance Learning (MIL) approach to image
classification. In particular we focus on a recent MIL method for binary classification where …

We introduce and test a binary classification method aimed at detecting malicious URL on
the basis of some information on both the URL syntax and its domain properties. Our method …

In this paper, we consider a binary supervised classification problem, called spherical
separation, that consists of finding, in the input space or in the feature space, a minimal …

We propose a heuristic approach for solving binary Multiple Instance Learning (MIL)
problems, whose objective is to categorize bags of instances. Considering the case with two …