'Bootstrapping' diaporamas de présentation

Peer-to-peer Communication Services

Peer-to-peer Communication Services. Henning Schulzrinne, Jae Woo Lee, Salman Baset Columbia University Wolfgang Kellerer, Zoran Despotovic DoCoMo Communications Laboratories Europe. Outline. Research overview Conceptual framework Four stages of p2p systems

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Bootstrapping Information Extraction with Unlabeled Data

Bootstrapping Information Extraction with Unlabeled Data. Rayid Ghani Accenture Technology Labs Rosie Jones Carnegie Mellon University & Overture. (With contributions from Tom Mitchell and Ellen Riloff). What is Information Extraction?.

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Intrusion Detection System WSN

Intrusion Detection System WSN. Tamer AbuHmed. Topics. IDS Challenges in WSN SPINS : Security Protocol for Sensor Network Efficient anonymity schemes for clustered wireless sensor networks. Placement Problem. Signature Update. IDS Challenges in WSN. Authentication.

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Estimating the reliability of a tree

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Bootstrapping

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Bootstrapping

Bootstrapping. Method for assessing confidence in nodes of a tree Create a new data set by resampling characters randomly with replacement New data set is the same size as the original Variation found from this method is similar to what would be found by collecting new data sets

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Variant definitions of pointer length in MDL

Variant definitions of pointer length in MDL. Aris Xanthos, Yu Hu, and John Goldsmith University of Chicago. Degrees of freedom in MDL modeling. MDL does not specify the form of the grammar being inferred. Carl de Marcken (1996)

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B-trust: Bayesian Trust Framework for Pervasive Computing

Daniele Quercia, Stephen Hailes, Licia Capra. CS department University College London {d.quercia}@cs.ucl.ac.uk. B-trust: Bayesian Trust Framework for Pervasive Computing. iTrust 2006. May 2006. Daniele Quercia. B-trust: Bayesian Trust Framework for Pervasive Computing. Outline.

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Flat-Panel PMT Front-End Requirements

Flat-Panel PMT Front-End Requirements. Bootstrapping: where from? Gain adaptation for large signals and variations Dynamic range and spill-over ADC layout On-board FPGA Conclusions. Stephan Eisenhardt University of Edinburgh. RICH Upgrade meeting, 16.04.2009.

280 views • 13 slides

Confidence Intervals

Confidence Intervals. Underlying model: Unknown parameter We know how to calculate point estimates E.g. regression analysis But different data would change our estimates. So, we treat our estimates as random variables Want a measure of how confident we are in our estimate.

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GBA support in R-UIM

GBA support in R-UIM. Jacques SEIF JSeif@axalto.com. Definition.

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Learning Subjective Nouns using Extraction Pattern Bootstrapping

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Learning Subjective Nouns using Extraction Pattern Bootstrapping

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Bootstrapping

Bootstrapping. James G. Anderson, Ph.D. Purdue University. Introduction. Bootstrapping is a statistical resampling method. Bootstrapping can be used to obtain empirical standard error estimates of model parameters in addition to the regular standard errors provided by the AMOS output.

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Sample enumeration: Forecasting from statistical models

Sample enumeration: Forecasting from statistical models . Dr Vernon Gayle and Dr Paul Lambert (Stirling University) Tuesday 29th April 2008. Communicating Results (to non-technically informed audiences). Davies (1992) Sample Enumeration Payne (1998) Labour Party campaign data

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CENTER FOR BIOLOGICAL SEQUENCE ANALYSIS

Bootstrapping Anders Gorm Pedersen Molecular Evolution Group Center for Biological Sequence Analysis Technical University of Denmark (DTU). CENTER FOR BIOLOGICAL SEQUENCE ANALYSIS. CENTER FOR BIOLOGICAL SEQUENCE ANALYSIS.

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Data Uncertainties :

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Latent model Deconvolves the hemodynamic response function using a small 2-param basis set (see Fig. 2)

BOLD Activity . Neural activity. A. A. Assumed HRF (Basis functions). B. B. C. C. D. D. Columbia Psychology SCAN group. Time (s). Time (s). http://www.scan.psych.columbia.edu/. Brain regions mediating a heat stimulus - pain report relationship.

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