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This paper deals with an application of a Fuzzy-ART self-organizing neural classiier to adaptive cate-gorization of the perceptual space of a mobile robot. The aim of the presented research is to develop a learning system for reactive locomotion control in an unknown, cluttered environment. A qualitative description of the proposed categorization technique(More)
It is often difficult to accurately predict when, why, and which patients develop shock, because signs of shock often occur late, once organ injury is already present. Three levels of aggregation of information can be used to aid the bedside clinician in this task: analysis of derived parameters of existing measured physiologic variables using simple(More)
OBJECTIVE The use of machine-learning algorithms to classify alerts as real or artifacts in online noninvasive vital sign data streams to reduce alarm fatigue and missed true instability. DESIGN Observational cohort study. SETTING Twenty-four-bed trauma step-down unit. PATIENTS Two thousand one hundred fifty-three patients. INTERVENTION Noninvasive(More)
Regularization is a principled way to control model complexity, prevent overfitting, and incorporate ancillary information into the learning process. As a convex relaxation of ℓ0-norm, ℓ1-norm regularization is popular for learning in high-dimensional spaces, where a fundamental assumption is the sparsity of model parameters. However, model sparsity can be(More)
In this paper, we address the question of what kind of knowledge is generally transferable from unlabeled text. We suggest and analyze the semantic correlation of words as a generally transferable structure of the language and propose a new method to learn this structure using an appropriately chosen latent variable model. This semantic correlation contains(More)