Results 1 - 10 of 118,359

Data networks

by L. Verger G, E. Gros D'aillon G, P. Major H, G. Németh H , 1992
"... a b s t r a c t In this paper we illustrate the core technologies at the basis of the European SPADnet project (www. spadnet.eu), and present the corresponding first results. SPADnet is aimed at a new generation of MRI-compatible, scalable large area image sensors, based on CMOS technology, that are ..."
Abstract - Cited by 2210 (5 self) - Add to MetaCart
, that are networked to perform gamma-ray detection and coincidence to be used primarily in (Time-of-Flight) Positron Emission Tomography (PET). The project innovates in several areas of PET systems, from optical coupling to single-photon sensor architectures, from intelligent ring networks to reconstruction

Learning Bayesian networks: The combination of knowledge and statistical data

by David Heckerman, David M. Chickering - Machine Learning , 1995
"... We describe scoring metrics for learning Bayesian networks from a combination of user knowledge and statistical data. We identify two important properties of metrics, which we call event equivalence and parameter modularity. These properties have been mostly ignored, but when combined, greatly simpl ..."
Abstract - Cited by 1158 (35 self) - Add to MetaCart
We describe scoring metrics for learning Bayesian networks from a combination of user knowledge and statistical data. We identify two important properties of metrics, which we call event equivalence and parameter modularity. These properties have been mostly ignored, but when combined, greatly

Using Bayesian networks to analyze expression data

by Nir Friedman, Michal Linial, Iftach Nachman - Journal of Computational Biology , 2000
"... DNA hybridization arrays simultaneously measure the expression level for thousands of genes. These measurements provide a “snapshot ” of transcription levels within the cell. A major challenge in computational biology is to uncover, from such measurements, gene/protein interactions and key biologica ..."
Abstract - Cited by 1088 (17 self) - Add to MetaCart
by showing how Bayesian networks can describe interactions between genes. We then describe a method for recovering gene interactions from microarray data using tools for learning Bayesian networks. Finally, we demonstrate this method on the S. cerevisiae cell-cycle measurements of Spellman et al. (1998). Key

Network data and measurement.

by Peter V Marsden - Annual Review of Sociology, , 1990
"... Abstract Data on social networks may be gathered for all ties linking elements of a closed population ("complete" network data) or for the sets of ties surrounding sampled individual units ("egocentric" network data). Network data have been obtained via surveys and questionnaire ..."
Abstract - Cited by 200 (0 self) - Add to MetaCart
Abstract Data on social networks may be gathered for all ties linking elements of a closed population ("complete" network data) or for the sets of ties surrounding sampled individual units ("egocentric" network data). Network data have been obtained via surveys

Cognitive networks

by Ryan W. Thomas, Luiz A. DaSilva, Allen B. MacKenzie - IN PROC. OF IEEE DYSPAN 2005 , 2005
"... This paper presents a definition and framework for a novel type of adaptive data network: the cognitive network. In a cognitive network, the collection of elements that make up the network observes network conditions and then, using prior knowledge gained from previous interactions with the network ..."
Abstract - Cited by 1106 (7 self) - Add to MetaCart
This paper presents a definition and framework for a novel type of adaptive data network: the cognitive network. In a cognitive network, the collection of elements that make up the network observes network conditions and then, using prior knowledge gained from previous interactions

A Delay-Tolerant Network Architecture for Challenged Internets

by Kevin Fall , 2003
"... The highly successful architecture and protocols of today’s Internet may operate poorly in environments characterized by very long delay paths and frequent network partitions. These problems are exacerbated by end nodes with limited power or memory resources. Often deployed in mobile and extreme env ..."
Abstract - Cited by 953 (12 self) - Add to MetaCart
expectations of end-to-end connectivity and node resources. The architecture operates as an overlay above the transport layers of the networks it interconnects, and provides key services such as in-network data storage and retransmission, interoperable naming, authenticated forwarding and a coarse

A Bayesian method for the induction of probabilistic networks from data

by Gregory F. Cooper, EDWARD HERSKOVITS - MACHINE LEARNING , 1992
"... This paper presents a Bayesian method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabili ..."
Abstract - Cited by 1400 (31 self) - Add to MetaCart
of probabilistic expert systems. We extend the basic method to handle missing data and hidden (latent) variables. We show how to perform probabilistic inference by averaging over the inferences of multiple belief networks. Results are presented of a preliminary evaluation of an algorithm for constructing a belief

The eyes have it: A task by data type taxonomy for information visualizations

by Ben Shneiderman - IN IEEE SYMPOSIUM ON VISUAL LANGUAGES , 1996
"... A useful starting point for designing advanced graphical user interjaces is the Visual lnformation-Seeking Mantra: overview first, zoom and filter, then details on demand. But this is only a starting point in trying to understand the rich and varied set of information visualizations that have been ..."
Abstract - Cited by 1265 (28 self) - Add to MetaCart
proposed in recent years. This paper offers a task by data type taxonomy with seven data types (one-, two-, three-dimensional datu, temporal and multi-dimensional data, and tree and network data) and seven tasks (overview, Zoom, filter, details-on-demand, relate, history, and extracts).

Collective classification in network data

by Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Gallagher, Tina Eliassi-Rad , 2008
"... Numerous real-world applications produce networked data such as web data (hypertext documents connected via hyperlinks) and communication networks (people connected via communication links). A recent focus in machine learning research has been to extend traditional machine learning classification te ..."
Abstract - Cited by 178 (32 self) - Add to MetaCart
Numerous real-world applications produce networked data such as web data (hypertext documents connected via hyperlinks) and communication networks (people connected via communication links). A recent focus in machine learning research has been to extend traditional machine learning classification

Support-Vector Networks

by Corinna Cortes, Vladimir Vapnik - Machine Learning , 1995
"... The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special pr ..."
Abstract - Cited by 3703 (35 self) - Add to MetaCart
properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the supportvector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data.
Results 1 - 10 of 118,359