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- Jussi Karlgren, Anders Holst, Magnus Sahlgren
- ECIR
- 2008

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)

- Anders Lansner, Anders Holst
- Int. J. Neural Syst.
- 1996

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 onelayer network implementing a naive Bayesian classifier. It… (More)

- David W Nelson, Björn Thornquist, +6 authors Eddie Weitzberg
- BMC medicine
- 2011

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)

- Anders Holst, Anders Lansner
- Int. J. Neural Syst.
- 1993

A query-reply system based on a Bayesian neural network is described. Strategies for generating questions which make the system both efficient and highly fault tolerant are presented. This involves having one phase of question generation intended to quickly reach a hypothesis followed by a phase where verification of the hypothesis is attempted. In… (More)

- David W Nelson, Anders Rudehill, +4 authors Bo-Michael Bellander
- Journal of neurotrauma
- 2012

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)

- SPIKING UNITS, Anders Lansner, Anders Holst
- 2000

We treat a Bayesian confidence propagation neural network, primarily in a classifier context. The onelayer 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)

- Tomas Olsson, Anders Holst
- FLAIRS Conference
- 2015

This paper presents a novel, unsupervised approach to detecting anomalies at the collective level. The method probabilistically aggregates the contribution of the individual anomalies in order to detect significantly anomalous groups of cases. The approach is unsupervised in that as only input, it uses a list of cases ranked according to its individual… (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)

- Anders Holst, Jan Ekman
- SCAI
- 2011