Based on URIs, HTTP and RDF, the Linked Data project [3] aims to expose, share and connect related data from diverse sources on the Semantic Web. Linked Open Data (LOD) is a community effort to apply the Linked Data principles to data published under open licenses. With this effort, a large number of LOD datasets have been gathered in the LOD cloud, such as DBpedia, Freebase and FOAF profiles. ...

BACKGROUND Statistical inference of signals is key to understand fundamental processes in the neurosciences. It is essential to distinguish true from random effects. To this end, statistical concepts of confidence intervals, significance levels and hypothesis tests are employed. Bootstrap-based approaches complement the analytical approaches, replacing the latter whenever these are not possible...

In this paper we discuss issues related to data mining from a noisy database such as what might be generated by a machine learning system. We describe an approach for estimating joint probability distributions of the noise-free case in terms of noisy observables and conditional probabilities which can be estimated using statistical sampling and error analysis. Several experiments are presented ...

AdaBoost [4] is a well-known ensemble learning algorithm that constructs its constituent or base models in sequence. A key step in AdaBoost is constructing a distribution over the training examples to create each base model. This distribution, represented as a vector, is constructed to be orthogonal to the vector of mistakes made by the previous base model in the sequence [6]. The idea is to ma...

A generalized maximum entropy based approach to noisy inverse problems such as the Abel problem, tomography, or deconvolution is discussed and reviewed. Unlike the more traditional regularization approach, in the method discussed here, each unknown parameter (signal and noise) is redefined as a proper probability distribution within a certain pre-specified support. Then, the joint entropies of ...

Security Inference from Noisy Data by Li Zhuang Doctor of Philosophy in Computer Science University of California, Berkeley Professor J. D. Tygar, Chair My thesis is that contemporary information systems allow automatic extraction of security-related information from large amounts of noisy data. Extracting this information is the security inference problem: attackers or defenders extract inform...

ICA (Independent Component Analysis) is a new technique for analyzing multi-variant data. Lots of results are reported in the field of neurobiological data analysis such as EEG (Electroencephalography), MRI (Magnetic Resonance Imaging), and MEG (Magnetoencephalography) using ICA. But there still remain problems. In most of the neurobiological data, there are a large amount of noise, and the num...