'Semi supervised' presentation slideshows

Gene family classification using a semi-supervised learning method

Nan Song Advisors: John Lafferty, Dannie Durand. Gene family classification using a semi-supervised learning method. Outline. Introduction A motivating application: genome annotation A graphical model of sequence relatedness Gene classification using machine learning

1.1k views • 87 slides

Learning from labelled and unlabeled data

958 views • 68 slides

Semi-Supervised Clustering

Semi-Supervised Clustering. Jieping Ye Department of Computer Science and Engineering Arizona State University http://www.public.asu.edu/~jye02. Outline. Overview of clustering and classification What is semi-supervised learning? Semi-supervised clustering Semi-supervised classification

1.01k views • 66 slides

Hypertext Data Mining (KDD 2000 Tutorial)

Hypertext Data Mining (KDD 2000 Tutorial). Soumen Chakrabarti Indian Institute of Technology Bombay http://www.cse.iitb.ernet.in/~soumen http://www.cs.berkeley.edu/~soumen soumen@cse.iitb.ernet.in. Hypertext databases. Academia Digital library, web publication Consumer

699 views • 60 slides

Hypertext Data Mining (KDD 2000 Tutorial)

Hypertext Data Mining (KDD 2000 Tutorial). Soumen Chakrabarti Indian Institute of Technology Bombay http://www.cse.iitb.ernet.in/~soumen http://www.cs.berkeley.edu/~soumen soumen@cse.iitb.ernet.in. Hypertext databases. Academia Digital library, web publication Consumer

662 views • 57 slides

Semi-supervised Structured Prediction Models

729 views • 56 slides

Semi-Supervised Learning in Gigantic Image Collections

Semi-Supervised Learning in Gigantic Image Collections. Rob Fergus (New York University) Yair Weiss (Hebrew University) Antonio Torralba (MIT). TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A A A A A A A A A. What does the world look like?.

659 views • 56 slides

Semi-Supervised Clustering

Semi-Supervised Clustering. CS 685: Special Topics in Data Mining Spring 2008 Jinze Liu. Outline of lecture. Overview of clustering and classification What is semi-supervised learning? Semi-supervised clustering Semi-supervised classification Semi-supervised clustering

559 views • 55 slides

Reasoning With Data Extracted From the Biomedical Literature

Reasoning With Data Extracted From the Biomedical Literature. William W. Cohen with Ni Lao (Google), Ramnath Balasubramanyan, Dana Moshovitz-Attias School of Computer Science, Carnegie Mellon University,. John Woolford, Jelena Jakovljevic Biology Dept, Carnegie Mellon University.

697 views • 52 slides

Lecture 14 Relation Extraction

Lecture 14 Relation Extraction. CSCE 771 Natural Language Processing. Topics Relation Extraction Readings: Chapter 22 NLTK 7.4-7.5. March 4, 2013. Overview. Last Time NER NLTK Chunking Example 7.4 (code_chunker1.py) , chinking Example 7.5 (code_chinker.py)

868 views • 52 slides

Constraints Driven Structured Learning with Indirect Supervision

Constraints Driven Structured Learning with Indirect Supervision. Dan Roth Department of Computer Science University of Illinois at Urbana-Champaign. With thanks to: Collaborators: Ming-Wei Chang, James Clarke, Dan Goldwasser , Lev Ratinov,

607 views • 51 slides

My past works

My past works. Kazunari Sugiyama 16 Apr., 2009 WING Group Meeting. Outline. I. Natural Language Processing (Disambiguation) I-1 Personal Name Disambiguation in Web Search Results I-2 Word Sense Disambiguation in Japanese Texts II. Web Information Retrieval

624 views • 50 slides

Richard Bourgon 20 February 2007 bourgon@ebi.ac.uk

Fine mapping of recombination in S. cerevisiae : high-density tiling arrays and semi-supervised clustering. Richard Bourgon 20 February 2007 bourgon@ebi.ac.uk. Meiotic recombination. Meiosis A diploid parent divides twice, yielding 4 haploid daughter cells.

606 views • 40 slides

373 views • 37 slides

Web Information Extraction

Web Information Extraction. ???. Outline. Background Approaches for generating wrappers Manually constructed Machine learning Examples Conclusion. Terminology. IE = Information Extractor WIE = Web Information Extractor TIE = Traditional Information Extractor. Background.

355 views • 35 slides

Jing Gao 1 , Wei Fan 2 , Deepak Turaga 2 , Olivier Verscheure 2 , Xiaoqiao Meng 2 ,

341 views • 23 slides

Semi-Supervised Learning With Graphs

289 views • 19 slides

Semi-supervised Dialogue Act Recognition

Semi-supervised Dialogue Act Recognition. Maryam Tavafi. Motivation. Detecting the human social intentions in spoken conversations Dialogue summarization Collaborative task learning agents Dialogue systems. Method for Semi-supervised DA modeling. SVM-hmm with bootstrapping

293 views • 15 slides

ACTIVE LEARNING FOR TEXT CLASSIFICATION

ACTIVE LEARNING FOR TEXT CLASSIFICATION. Ankit Bhutani Y9094. AUTOMATIC TEXT CLASSIFICATION. A FEW HOURS ONLY. MANUAL TEXT CLASSIFICATION. TAKES YEARS. ORGANIZING LARGE VOLUMES OF TEXT. Massive volume of online text available. Organisation into categories to enable efficient search.

288 views • 12 slides

Wireless and Mobile Access to Digital Libraries

Wireless and Mobile Access to Digital Libraries. Sang-goo Lee School of Computer Science and Engineering Seoul National University Seoul, Korea. Worldwide Market. Year 2000 to year 2007 subscribers: 530 million to 1,370 million service revenue: $295 billion to $521 billion

181 views • 12 slides