Empirical evidence shows that in favorable situations semi-supervised learning (SSL) algorithms can capitalize on the abundance of unlabeled training data to improve the performance of a learning task, in the sense that fewer labeled training data are needed to achieve a target error bound. However, in other situations unlabeled data do not seem to help. Recent attempts at theoretically charact...

This paper investigates the use of semi-supervised learning (SSL) for predicting Alzheimers Disease (AD) conversion in Mild Cognitive Impairment (MCI) patients based on Magnetic Resonance Imaging (MRI). SSL methods differ from standard supervised learning methods in that they make use of unlabeled data in this case data from MCI subjects whose final diagnosis is not yet known. We compare two wi...

Empirical evidence shows that in favorable situations semi-supervised learning (SSL) algorithms can capitalize on the abundance of unlabeled training data to improve the performance of a learning task, in the sense that fewer labeled training data are needed to achieve a target error bound. However, in other situations unlabeled data do not seem to help. Recent attempts at theoretically charact...

This paper describes the structure and evaluation of the syntactico-semantic lexicon (SSL) of the German Natural Language Understanding System VIE-LANG [3]. VIE-LANG uses an SI-Net [2] as internal representation. The SSL contains the rules according to which the mapping between net-structures and surface structures of a sentence is carried out. This information is structured in a way that it ca...

We propose a general approach to modeling semisupervised learning (SSL) algorithms. Specifically, we present a declarative language for modeling both traditional supervised classification tasks and many SSL heuristics, including both well-known heuristics such as co-training and novel domainspecific heuristics. In addition to representing individual SSL heuristics, we show that multiple heurist...

Abstmct -The focus of this paper is semi-supervised learning in the context of pattern recognition. Semi-supervised learning (SSL) refers to the semi-supervised construction of clusters during the training phase of exemplar-based classifiers. Using artificially generated data sets we present experimental results of classifiers that follow the SSL paradigm and we show that, especially for diffic...

SSL/TLS is currently the most deployed security protocol on the Internet. SSL/TLS provides end-to-end secure communications between two entities with authentication and data protection. However, what is missing from the protocol is a way to provide the non-repudiation service. In this paper, we describe a generic implementation of the non-repudiation service as an optional module in the SSL/TLS...

⎯ Business-to-Business and Business-to-Customer transactions in Internet require secure communication, especially for web applications. The Secure Socket Layer (SSL) protocol is one of the most viable solutions to provide the required level of confidentiality, message integrity and endpoint authentication. The two main alternatives for providing SSL security are the endto-end and the accelerate...

The abundance of unlabeled data makes semi-supervised learning (SSL) an attractive approach for improving the accuracy of learning systems. However, we are still far from a complete theoretical understanding of the benefits of this learning scenario in terms of sample complexity. In particular, for many natural learning settings it can in fact be shown that SSL does not improve sample complexit...

In today’s world wide web hundreds of thousands of companies use SSL to protect their customers’ transactions from potential eavesdroppers. Recently, a new attack against the common usage of SSL surfaced, SSL stripping. The attack is based on the fact that users almost never request secure pages explicitly but rather rely on the servers, to redirect them to the appropriate secure version of a p...