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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 show that sequence information can be encoded into high-dimensional fixed-width vectors using permutations of coordinates. Computational models of language often represent words with high-dimensional semantic vectors compiled from word-use statistics. A word's semantic vector usually encodes the contexts in which the word appears in a large body of text(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)
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)
Sympathetic neurons depend on the classical neurotrophin nerve growth factor (NGF) for survival by the time they innervate their targets, but not before. The acquisition of NGF responsiveness is thought to be controlled by environmental cues in sympathetic neurons. We have investigated the expression of the signal transducing NGF receptor trkA on mRNA and(More)
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)
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)