'Error matrix' presentation slideshows

Multivariate Analysis of Variance (MANOVA)

Multivariate Analysis of Variance (MANOVA). Outline. Purpose and logic : page 3 Hypothesis testing : page 6 Computations: page 11 F -Ratios: page 25 Assumptions and noncentrality : page 35. MANOVA. When ?

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431 views • 29 slides

KalFitAlg: recent developments and tests

KalFitAlg: recent developments and tests. WANG Dayong Jan.18,2006. Outline. Recent developments and some tests Status and usage Present performances Existing problems and possible solutions The next to do. A retrospect: what we achieved for Boss5.0.0(Nov,2005).

470 views • 29 slides

Visual Interpretation of Aerial Imagery

Visual Interpretation of Aerial Imagery Introduction to Remote Sensing GISC 6325 / GEOS 5325 Dr. Stuart Murchison Photointerpreter, Image Analyst Now means interpretation from paper photographs as well as digital images, It is based on a priori knowledge and experience, Deals with:

3.06k views • 27 slides

15-211 Fundamental Structures of Computer Science

15-211 Fundamental Structures of Computer Science. Data Compression with SVD. Data Compression. We have studied two important data compression algorithms Huffman Code Lemple-Ziv Dictionary Method. Provides a good introduction to lossless compression.

379 views • 26 slides

Translation from Near to Far at K2K

July 4, 2002 n Fact02 @Imperial College London. Translation from Near to Far at K2K. T.Kobayashi IPNS, KEK for K2K beam monitor group (K.Nishikawa, T.Hasegawa, T.Inagaki, T.Maruyama, T.Nakaya, ....)

394 views • 26 slides

Recent results from Kalman track fitting

Recent results from Kalman track fitting. WANG Dayong Oct.26,2005. outline. Progress and status of KalFit Some words for memory leakage problem Present performance of the package. Progress and status of KalFit. Software re-arrange and evolvement Improved event model for TDS and DST

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VXDBasedReco TRACK RECONSTRUCTION PERFORMANCE STUDIES

839 views • 21 slides

Land Cover Classification from Remote Sensing Imagery: Revisiting and Evaluating Classification Accuracy

Land Cover Classification from Remote Sensing Imagery: Revisiting and Evaluating Classification Accuracy. Ramita Manandhar, Inakwu Odeh Faculty of Agriculture, Food and Natural Resources The University of Sydney, NSW, Australia. Significance:.

596 views • 21 slides

An example: Stand biomass estimation by LiDAR

-10. -5. 0. 5. 10. Height Growth [m/3yr]. An example: Stand biomass estimation by LiDAR. Stand biomass by LiDAR & Stand Density Chart. Stand Height. RMSE = 53.0 m 3 /ha Bias = -20.9 m 3 /ha Ratio of contribution = 0.94. Stand Density. Biomass. LiDAR Large footprint.

327 views • 19 slides

Local Muon Tracks and Beyond

Local Muon Tracks and Beyond. the new muo_trackreco results of the fit algorithm :. efficiencies, resolutions for local track finding preliminary studies of the new muo_trackreco performances errors. association with central tracks (global fit):. algorithm status and plans.

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239 views • 14 slides

Uncertainties about Uncertainties

274 views • 13 slides

A.K.Ichikawa 2008/1/17

Proposal for the study to define what is really necessary and what is not when the data from beam, ND and SK are combined. A.K.Ichikawa 2008/1/17. Prediction of flux at SK with error matrix by beam MC. Simple parameterization or modeling is the key for success, I believe.

188 views • 11 slides

Accuracy Assessment

Accuracy Assessment. Having produced a map with classification is only 50% of the work, we need to quantify how good the map is. This step is called the accuracy assessment, meaning we need to evaluate the classes with independent data. Depending on

1.17k views • 11 slides

muo_trackreco (1)

muo_trackreco (1). http://d0-france.in2p3.fr/WORKING_GROUPS/MUONS/muons.html. fit algorithm : algorithm run in WAMUS and FAMUS use a stand alone non linear fit class when fit fails: returns first momentum estimation compute the error matrix. muo_trackreco (2). what is new :

84 views • 8 slides