Densely Supervised Grasp Detector (DSGD)

A semi-supervised Spam mail detector

This document describes a novel semi-supervised approach to spam classification, which was successful at the ECML/PKDD 2006 spam classification challenge. A local learning method based on lazy projections was successfully combined with a variant of a standard semisupervised learning algorithm.

Semi-supervised Object Detector Learning from Minimal Labels

While traditional machine learning approaches to classification involve a substantial training phase with significant number of training examples, in a semi-supervised setting, the focus is on learning the trends in the data from a limited training set and simultaneously using the trends learned to label unlabeled data. This report focuses on a particular semi-supervised learning technique call...

Supervised algorithms for particle classification by a transition radiation detector

Support Vector Machines (SVM) provide an interesting computational paradigm for the classification of data from high energy physics and particle astrophysics experiments. In this study the classification power of support vector machines is compared with those from standard supervised algorithms, i.e. likelihood ratio (LR) and artificial neural networks (ANN), using test beam data from the trans...

Densely Entangled Financial Systems

In [1] Zawadoski introduces a banking network model in which the asset and counter-party risks are treated separately and the banks hedge their assets risks by appropriate OTC contracts. In his model, each bank has only two counter-party neighbors, a bank fails due to the counter-party risk only if at least one of its two neighbors default, and such a counter-party risk is a low probability eve...

Grasp.