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CSA3180: Natural Language Processing

CSA3180: Natural Language Processing

CSA3180: Natural Language Processing. Text Processing 3 – Double Lecture Discovering Word Associations Text Classification TF.IDF Clustering/Data Mining Linear and Non-Linear Classification Binary Classification Multi-Class Classification. Introduction.

By ivanbritt
(334 views)

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

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

DETERMINATION OF THE PROVENANCE OF VINICA TERRA COTTA ICONS USING SUPPORT VECTOR MACHINES. Vinka Tanevska, Igor Kuzmanovski*, Orhideja Grupče and Biljana Minčeva-Šukarova. Institut za hemija, PMF, Univerzitet “Sv. Kiril i Metodij”, Arhimedova 5, 1001 Skopje, Republic of Macedonia

By niveditha
(311 views)

Linear Discriminant Analysis and Its Variations

Linear Discriminant Analysis and Its Variations

Linear Discriminant Analysis and Its Variations. Abu Minhajuddin CSE 8331. Department of Statistical Science Southern Methodist University April 27, 2002. Plan…. The Problem Linear Discriminant Analysis Quadratic Discriminant Analysis Other Extensions Evaluation of the Method

By zev
(242 views)

What do you do when you know that you don’t know?

What do you do when you know that you don’t know?

What do you do when you know that you don’t know?. Abhijit Bendale*, Terrance Boult Samsung Research America* University of Colorado of Colorado Springs. Facial Attributes.

By aitana
(86 views)

Automated Bias Detection in Journalism

Automated Bias Detection in Journalism

Automated Bias Detection in Journalism. Richard Lee, James Chen, Jason Cho. MOTIVATION. PROBLEM STATEMENT. RESULTS, CONT. Assuming we’re given parallel corpuses, identify biased sentences. From there, identify biased articles.

By aleda
(104 views)

Selective Sampling on Probabilistic Labels

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

By lorna
(80 views)

Interpolants as Classifiers

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

By oriana
(82 views)

Data Mining and Semantic Web

Data Mining and Semantic Web

University of Belgrade School of Electrical Engineering Chair of Computer Engineering and Information Theory. Data Mining and Semantic Web. Neural Networks: Backpropagation algorithm. Miroslav Ti šma tisma.etf @gmail.com. But the camera sees this:. What is this?. You see this: .

By dawson
(108 views)

Using Weka for Obesity

Using Weka for Obesity

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

By tekli
(89 views)

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

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

CS 546 Machine Learning in NLP Structured Prediction:   Theories and Applications to Natural Language Processing. Dan Roth Department of Computer Science University of Illinois at Urbana-Champaign. What’s the class about How I plan to teach it Requirements Questions/Time? .

By lenora
(116 views)

For Tuesday

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.

By piper
(86 views)

“Cheap” Tricks for NLP: An “Invited” Talk

“Cheap” Tricks for NLP: An “Invited” Talk

“Cheap” Tricks for NLP: An “Invited” Talk. Craig Martell Associate Professor Naval Postgraduate School Director, NLP Lab. Overview. We’ve been doing work on microtext since before it was “ microtext ”. About NPS NPS Chat Corpus (v1 and v2?) Overview Goal (Jane Lin, NSA)

By bevis
(117 views)

Performance Indices for Binary Classification

Performance Indices for Binary Classification

Performance Indices for Binary Classification. 張智星 (Roger Jang) jang@mirlab.org http://mirlab.org/jang 多媒體資訊檢索實驗室 台灣大學 資訊工程系. Confusion Matrix for Binary Classification. Terminologies used in a confusion matrix. Commonly used formulas. Predicted. 0: negative. 1: positive.

By nida
(151 views)

Classification

Classification

Classification. Logistic Regression. Machine Learning. Classification. Email: Spam / Not Spam? Online Transactions: Fraudulent (Yes / No)? Tumor: Malignant / Benign ?. 0: “Negative Class” (e.g., benign tumor) 1: “Positive Class” (e.g., malignant tumor). (Yes) 1. Malignant ?. (No) 0.

By lemuel
(125 views)

Writer identification in offline handwriting UCML 2013

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

By rianna
(142 views)

Context and Learning in Multilingual Tone and Pitch Accent Recognition

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

By fauve
(126 views)

I256 Applied Natural Language Processing Fall 2009

I256 Applied Natural Language Processing Fall 2009

I256 Applied Natural Language Processing Fall 2009. Lecture 10 Classification. Barbara Rosario. Today. Classification tasks Various issues regarding classification Clustering vs. classification, binary vs. multi-way, flat vs. hierarchical classification, variants…

By magnar
(116 views)

Multi-Label Feature Selection for Graph Classification

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 .

By dympna
(284 views)

Using Micro-Reviews to Select an Efficient Set of Review

Using Micro-Reviews to Select an Efficient Set of Review

Using Micro-Reviews to Select an Efficient Set of Review. Date : 2014/01/14 Author : Thanh -Son Nguyen, Hady W. Lauw , Panayiotis Tsaparas Source : CIKM’13 Advisor : Jia -ling Koh Speaker : Shao-Chun Peng. Outline. Introduction Method Experiments Conclusion.

By branxton
(87 views)

Context and Learning in Multilingual Tone and Pitch Accent Recognition

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

By evette
(154 views)

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