'Labeled data' diaporamas de présentation

Rapid Training of Information Extraction with Local and Global Data Views

Rapid Training of Information Extraction with Local and Global Data Views. Committee Prof. Ralph Grishman Prof. Satoshi Sekine Prof. Heng Ji Prof. Ernest Davis Prof. Lakshminarayanan Subramanian. Dissertation Defense Ang Sun Computer Science Department

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Overview of Data Mining

Overview of Data Mining. Mehedy Masud Lecture slides modified from: Jiawei Han ( http://www-sal.cs.uiuc.edu/~hanj/DM_Book.html ) Vipin Kumar ( http://www-users.cs.umn.edu/~kumar/csci5980/index.html ) Ad Feelders ( http://www.cs.uu.nl/docs/vakken/adm/ )

890 views • 75 slides

Interactive Machine Learning: Leveraging Human Intelligence

Interactive Machine Learning: Leveraging Human Intelligence. Dan R. Olsen Jr. Brigham Young University Dept. of Computer Science. Interactive Machine Learning (IML). More questions than answers. Why is IML of Interest?. Why is IML of Interest?. Exponential Growth Computer processor speed

637 views • 55 slides

Data and Databases

Data and Databases. The Data Basics. Data Facts concerning things such as people, objects, or events Information data that have been processed and presented in a form suitable for human interpretation Database a collection of interrelated , shared , and controlled data.

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A Survey on Transfer Learning

A Survey on Transfer Learning. Sinno Jialin Pan Department of Computer Science and Engineering The Hong Kong University of Science and Technology Joint work with Prof. Qiang Yang. Transfer Learning? (DARPA 05). Transfer Learning (TL):

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Kshitij Judah, Alan Fern, Tom Dietterich

Active Imitation Learning via State Queries. Kshitij Judah, Alan Fern, Tom Dietterich. School of EECS, Oregon State University. TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: . Preliminaries.

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Unsupervised Constraint Driven Learning for Transliteration Discovery

Unsupervised Constraint Driven Learning for Transliteration Discovery. M. Chang, D. Goldwasser, D. Roth, and Y. Tu. What I am going to do today… . Goal 1 : Present the transliteration work Get feedback! Goal 2: Think about this work with CCM Tutorial …. 

464 views • 34 slides

SET (6)

SET (6). Prof. Dragomir R. Radev radev@umich.edu. Vector space classification. x2. topic2. topic1. x1. Decision surfaces. x2. topic2. topic1. x1. Decision trees. x2. topic2. topic1. x1. Classification using decision trees. Expected information need

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Transfer for Supervised Learning Tasks

Transfer for Supervised Learning Tasks. HAITHAM BOU AMMAR MAASTRICHT UNIVERSITY. Motivation. Model. Assumptions: Training and Test are from same distribution Training and Test are in same feature space. Not True in many real-world applications. Training Data. ?. Test Data.

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The Informational Complexity of Interactive Machine Learning

The Informational Complexity of Interactive Machine Learning. Steve Hanneke. Passive Learning. Data Source. Expert / Oracle. Learning Algorithm. Raw Unlabeled Data. Labeled examples. Algorithm outputs a classifier. Learning by Interaction: The Big Picture. Data Source.

494 views • 33 slides

RiskCog: Privacy-preserving Unobtrusive Real-time Mobile User Authentication

RiskCog: Privacy-preserving Unobtrusive Real-time Mobile User Authentication. Yan Chen Lab of Internet and Security Technology (LIST) Zhejiang University, China Northwestern University, USA. How many of you use your smartphones to make payments? What about to storing sensitive data

510 views • 29 slides

Text Normalization

Text Normalization. Lecture 19 Spoken Language Processing Prof. Andrew Rosenberg. Text Normalization.

478 views • 27 slides

Combining Labeled and Unlabeled Data for Multiclass Text Categorization

Combining Labeled and Unlabeled Data for Multiclass Text Categorization. Rayid Ghani Accenture Technology Labs. Supervised Learning with Labeled Data. Labeled data is required in large quantities and can be very expensive to collect. Why use Unlabeled data?. Very Cheap in the case of text

364 views • 26 slides

Data Mining and Bioinformatics

classify biological sequences SVM, Neural Nets, Decision Trees, Rules

1.14k views • 24 slides

Presenter: Lung-Hao Lee ( ??? ) January 7, 2010@Room 309

Improving Web Page Classification by Label-propagation over Click Graphs Soo -Min Kim, Patrick Pantel , Lei Duan and Scott Gaffney Yahoo ! Labs CIKM 2009. Presenter: Lung-Hao Lee ( ??? ) January 7, 2010@Room 309. Outlines.

386 views • 23 slides

Named Entity Recognition based on Bilingual Co-training

Named Entity Recognition based on Bilingual Co-training. Li Yegang lyg8256@bit.edu.cn School of Computer, BIT. Outline. Introduction and Related Work Bilingual Co-training Corrective NE Projection Annotation Experiment Result. 1.Introduction. Related work Das et al.,2011

354 views • 22 slides

Data Mining Practical Machine Learning Tools and Techniques

Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 11, Beyond supervised and unsupervised learning of Data Mining by I. H. Witten, E. Frank, M. A. Hall and C. J. Pal. Semi-supervised and multi-instance learning. Semisupervised learning

460 views • 17 slides

The Viola/Jones Face Detector

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Machine Learning Theory

Machine Learning Theory. Lecture 1, Jan. 12 th 2010. Maria-Florina Balcan. Machine Learning. Image Classification. Document Categorization. Speech Recognition. Protein Classification. Spam Detection. Branch Prediction. Fraud Detection. Playing Games. Computational Advertising.

256 views • 10 slides

Transfer Learning for Image Classification

Group Project for EE5561 09 Spring. Transfer Learning for Image Classification. Group No.: 15 Group member : Feng Cai caixx043@umn.edu Sauptik Dhar dharx007@umn.edu Jingying Lin linxx634@umn.edu. B RIEF O UTLINE.

748 views • 8 slides