'Binary classification' diaporamas de présentation

CSA3180: Natural Language Processing

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I256 Applied Natural Language Processing Fall 2009

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For Tuesday

For Tuesday. Read chapter 18, section 7 Read chapter 19, sections 1-2 and 5 Homework: Chapter 18, exercises 6 and 11. Program 3. Any questions?. Empirical Evaluation. Training and Testing Leave-One-Out Cross-validation Learning Curves. Decision Trees.

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Context and Learning in Multilingual Tone and Pitch Accent Recognition

Context and Learning in Multilingual Tone and Pitch Accent Recognition. Gina-Anne Levow University of Chicago May 18, 2007. Roadmap. Challenges for Tone and Pitch Accent Contextual effects Training demands Modeling Context for Tone and Pitch Accent Data collections & processing

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Context and Learning in Multilingual Tone and Pitch Accent Recognition

Context and Learning in Multilingual Tone and Pitch Accent Recognition. Gina-Anne Levow University of Chicago May 18, 2007. Roadmap. Challenges for Tone and Pitch Accent Contextual effects Training demands Modeling Context for Tone and Pitch Accent Data collections & processing

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Selective Sampling on Probabilistic Labels

Selective Sampling on Probabilistic Labels. Peng Peng , Raymond Chi-Wing Wong CSE, HKUST. Outline. Introduction Motivation Contributions Methodologies Theory Results Experiments Conclusion. Introduction. Binary Classification Learn a classifier based on a set of labeled instances

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Classification

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Dan Roth Department of Computer Science University of Illinois at Urbana-Champaign

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Multi-Label Feature Selection for Graph Classification

Multi-Label Feature Selection for Graph Classification. Xiangnan Kong, Philip S. Yu. Department of Computer Science University of Illinois at Chicago. Outline. Introduction Multi-Label Feature Selection for Graph Classification Experiments Conclusion. Introduction: Graph Data .

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Using Micro-Reviews to Select an Efficient Set of Review

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Interpolants as Classifiers

Interpolants as Classifiers. Rahul Sharma Joint work with Aditya Nori (MSR India) and Alex Aiken (Stanford). Interpolants. If then an interpolant satisfies: contains only the variables common to and An interpolant is a simple proof

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Data Mining and Semantic Web

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Using Weka for Obesity

Using Weka for Obesity. SaToya Kelliebrew Midterm for 675 Artificial Intelligence March 6, 2013. Figure 1. Binary Classification. Questions????.

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Linear Discriminant Analysis and Its Variations

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Writer identification in offline handwriting UCML 2013

Divya Raj, Rashmi Mishra, Kavita Sheth , Anusha Buchireddygari , Poonam Ekh e l i kar. Writer identification in offline handwriting UCML 2013. Machine . Overview. Problem definition Real time application Classes of handwriting recognition Data and representation Features

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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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DETERMINATION OF THE PROVENANCE OF VINICA TERRA COTTA ICONS USING SUPPORT VECTOR MACHINES

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Automated Bias Detection in Journalism

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