'Confusion matrix' presentation slideshows

Ensembles for Cost-Sensitive Learning

Ensembles for Cost-Sensitive Learning. Thomas G. Dietterich Department of Computer Science Oregon State University Corvallis, Oregon 97331 http://www.cs.orst.edu/~tgd. Outline. Cost-Sensitive Learning Problem Statement; Main Approaches Preliminaries Standard Form for Cost Matrices

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Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on

Learning Globally-Consistent Local Distance Functions for Shape-Based Image Retrieval and Classification. Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on Andrea Frome , EECS, UC Berkeley Yoram Singer, Google, Inc Fei Sha , EECS, UC Berkeley

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Classification, Detection and Segmentation of Deformable Animals in Images

Classification, Detection and Segmentation of Deformable Animals in Images. Omkar M. Parkhi 200807012. Advisers: Prof. C.V. Jawahar Prof. A. P.Zisserman 3 rd August 2011. Popular in the community since long time. Several datasets such as Pascal VOC, Caltech, Imagenet have

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Sreekar Krishna Committee: Dr. Sethuraman ( Panch ) Panchanathan , Chair

CUbiC. C ENTER FOR C OGNITIVE U BIQUITOUS C OMPUTING. Social Interaction Assistant An Assistive and Rehabilitative Technology to Enrich the Social Interactions for Individuals who are Blind and Visually Impaired. Sreekar Krishna Committee: Dr. Sethuraman ( Panch ) Panchanathan , Chair

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Modeling and Segmentation

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Implementing Query Classification

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Scaling multi-class Support Vector Machines using inter-class confusion

Scaling multi-class Support Vector Machines using inter-class confusion. Author:Shantanu Sunita Sarawagi Soumen Chakrabarti Advisor:Dr Hsu Graduate:ching-wen Hong. Content. 1.Motivation 2.Objective

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Volcanic ash retrieval from IR multispectral measurements by means of Neural Networks: an analysis of the Eyjafjallajokull eruption.

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Native Judgments of Non-Native Usage: Experiments in Preposition Error Detection

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Evaluating Classifiers

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Geog 458: Map Sources and Errors

Geog 458: Map Sources and Errors. Uncertainty January 23, 2006. Outlines. Defining uncertainty How to calculate uncertainty? Nominal case: Confusion matrix Interval/ratio case: RMSE How to validate uncertainty? Internal validation: MAUP External validation: Conflation.

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A Semantic Text Classification Based on DBpedia

AST2009. A Semantic Text Classification Based on DBpedia. Rujiang Bai, Junhua Liao Shandong University of Technology Library Zibo 255049, China { brj, ljhbrj}@sdut.edu.cn. OUTLINE. 1.BACKGROUND 2. DBpedia 3.OUR PROPOSED METHODS 4.EXPERIMENT 5.CONCLUSION.

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Current HOARSE related activities

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Cross Validation False Negatives / Negatives

Cross Validation False Negatives / Negatives. ?"? ??? ???????. ??????. False Positives / Negatives. Confusion matrix 1. Confusion matrix 2. FN. Actual. Actual. FP. Predicted. Predicted. Precision (P) = 20 / 50 Recall (P) = 20 / 30. Example. Precision (A) = 50% (500/1000)

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Algorithms and Applications in Computer Vision

Algorithms and Applications in Computer Vision. Lihi Zelnik -Manor lihi@ee.technion.ac.il ROC. Precision-Recall. circles. Not circles. Truth:. Not circles. Result:. circles. Precision-Recall. circles. Not circles. Truth:. Not circles. FN. Result:. circles. TP. FP. TN.

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Human Action Recognition Week 10

Taylor Rassmann. Human Action Recognition Week 10. Overview Human Action Recognition. UCF50 dataset Begin with optical flow Lucas- Kanade with pyramids. Overview Human Action Recognition: Features. Kinematic Features Divergence Vorticity Symmetric Flow Fields Asymmetric Flow Fields.

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Stanford Parser

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Performance Indices for Binary Classification

Performance Indices for Binary Classification. ??? (Roger Jang) jang@mirlab.org http://mirlab.org/jang ?????????? ???? ?????. Confusion Matrix for Binary Classification. Terminologies used in a confusion matrix. Commonly used formulas. Predicted. 0: negative. 1: positive.

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