This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias

Mobile robot localization is the problem of determining a robot's pose from sensor data. This article presents a family of probabilistic localization algorithms known as Monte Carlo Localization (MCL). MCL algorithms represent a robot's belief by a set of weighted hypotheses (samples

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in these networks. In this paper, we review localization techniques and evaluate the effectiveness of a very simple connectivity-metric method for localization in outdoor environments that makes use of the inherent radio-frequency (RF) communications capabilities of these devices. A fixed number of reference points

Most current statistical natural language processing models use only local features so as to permit dynamic programming in inference, but this makes them unable to fully account for the long distance structure that is prevalent in language use. We show how to solve this dilemma with Gibbs sampling

Recently, methods based on local image features have shown promise for texture and object recognition tasks. This paper presents a large-scale evaluation of an approach that represents images as distributions (signatures or histograms) of features extracted from a sparse set of keypoint locations

distortion--robustness tradeoffs than currently popular spread-spectrum and low-bit(s) modulation methods. Furthermore, we show that for some important classes of probabilistic models, DC-QIM is optimal (capacity-achieving) and regular QIM is near-optimal. These include both additive white Gaussian noise

-organizing, hierarchical, master-slave configuration synchronizes local clocks within the subnet and to national time standards via wire or radio. The servers can also redistribute time information within a network via local routing algorithms and time daemons. The architectures, algorithms and protocols which have

This paper discusses the prevalence of Silicon Valley–style localiza-tions of individual manufacturing industries in the United States. A model in which localized industry-specific spillovers, natural ad-vantages, and pure random chance all contribute to geographic concentration is used to develop