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Latent Semantic Analysis is a method of computing high-dimensional semantic vectors, or context vectors, for words from their co-occurrence statistics. An experiment by Landauer & Dumais (1997) covers a vocabulary of 60,000 words (unique letter strings delimited by word-space characters) in 30,000 contexts (text samples or \documents" of about 150 words… (More)

We treat a Bayesian confidence propagation neural network, primarily in a classifier context. The one-layer version of the network implements a naive Bayesian classifier, which requires the input attributes to be independent. This limitation is overcome by a higher order network. The higher order Bayesian neural network is evaluated on a real world task of… (More)

- Anders Holst
- 1997

This thesis deals with a Bayesian neural network model. The focus is on how to use the model for automatic classification, i.e. on how to train the neural network to classify objects from some domain, given a database of labeled examples from the domain. The original Bayesian neural network is a one-layer network implementing a naive Bayesian classifier. It… (More)

Word space models, in the sense of vector space models built on distributional data taken from texts, are used to model semantic relations between words. We argue that the high dimensionality of typical vector space models lead to unintuitive effects on modeling likeness of meaning and that the local structure of word spaces is where interesting semantic… (More)

BACKGROUND
Cerebral microdialysis (MD) is used to monitor local brain chemistry of patients with traumatic brain injury (TBI). Despite an extensive literature on cerebral MD in the clinical setting, it remains unclear how individual levels of real-time MD data are to be interpreted. Intracranial pressure (ICP) and cerebral perfusion pressure (CPP) are… (More)

This report deals with a Bayesian neural network in a classiier context. In our network model, the units represent stochastic events, and the state of the units are related to the probability of these events. The basic Bayesian model is a one-layer neural network, which calculates the posterior probabilities of events, given some observed, independent… (More)

- Jan Ekman, Anders Holst
- 2008

This report concerns the "ISC-tool", a tool for classication of patterns and detection of anomalous patterns, where a pattern is a set of values. The tool has a graphical user interface "the anomalo-meter" that shows the degree of anomaly of a pattern and how it is classied. The report describes the user interaction with the tool and the underlying… (More)

In this paper we describe how Bayesian Principal Anomaly Detection (BPAD) can be used for detecting long and short term trends and anomalies in geographically tagged alarm data. We elaborate on how the detection of such deviations can be used for high-lighting suspected criminal behavior and activities. BPAD has previously been successively deployed and… (More)

Traumatic brain injury (TBI) is a major cause of morbidity and mortality. Identifying factors relevant to outcome can provide a better understanding of TBI pathophysiology, in addition to aiding prognostication. Many common laboratory variables have been related to outcome but may not be independent predictors in a multivariate setting. In this study, 757… (More)