'Similarity based' presentation slideshows

Agenda

Agenda. Challenges & Basic Notions Part I: Large Process Models Part II: Large Process Model Collections Part III: Large Process Structures References. Business Process Repositories Basic Challenge: Dealing with Large Model Collections. Business Process Repositories.

1.36k views • 122 slides

Personalizing the Web: Building effective recommender systems

Personalizing the Web: Building effective recommender systems. Bamshad Mobasher Center for Web Intelligence School of Computer Science, Telecommunication, and Information Systems DePaul University, Chicago, Illinois, USA. Outline. Web Personalization & Recommender systems

960 views • 75 slides

Meta-Learning : t he future of data mining

Meta-Learning : t he future of data mining. W?odzis?aw Duch & Co Department of Informatics, Nicolaus Copernicus University, Toru?, Poland School of Computer Engineering, Nanyang Technological University, Singapore Google: Duch W University of Lausanne , 4 /201 2. Toru ?. Toru ?.

810 views • 68 slides

Meta-Learning : t he future of data mining

Meta-Learning : t he future of data mining. W?odzis?aw Duch & Co Department of Informatics, Nicolaus Copernicus University, Toru?, Poland School of Computer Engineering, Nanyang Technological University, Singapore Google: Duch W University of Lausanne , 4 /201 2. Toru ?. Toru ?.

793 views • 68 slides

Meta-Learning : t he future of data mining

Meta-Learning : t he future of data mining. W?odzis?aw Duch & Co Department of Informatics, Nicolaus Copernicus University, Toru?, Poland School of Computer Engineering, Nanyang Technological University, Singapore Google: W. Duch INFER workshop , 4 /201 2.

1.09k views • 65 slides

Gene prediction

Gene prediction. Gene Prediction: Computational Challenge.

772 views • 64 slides

Personalizing the Web: Building effective recommender systems

Personalizing the Web: Building effective recommender systems. Bamshad Mobasher Center for Web Intelligence School of Computer Science, Telecommunication, and Information Systems DePaul University, Chicago, Illinois, USA. Outline. Web Personalization & Recommender systems

764 views • 63 slides

Meta-Learning : t he future of data mining

Meta-Learning : t he future of data mining. W?odzis?aw Duch & Collaborators Department of Informatics, Nicolaus Copernicus University, Toru?, Poland School of Computer Engineering, Nanyang Technological University, Singapore Google: Duch W Jiao Tong University, 12/2010.

784 views • 62 slides

Meta-Learning : t he future of data mining

Meta-Learning : t he future of data mining. W?odzis?aw Duch Norbert Jankowski, Krzysztof Gr?bczewski + Tomasz Maszczyk + Marek Grochowski Department of Informatics, Nicolaus Copernicus University , Toru ? , Poland Google: W. Duch WCCI 2010 , Barcelona.

617 views • 61 slides

654 views • 51 slides

546 views • 39 slides

Minimal Loss Hashing for Compact Binary Codes

Minimal Loss Hashing for Compact Binary Codes. Mohammad Norouzi David Fleet University of Toronto. Near Neighbor Search. Near Neighbor Search. Near Neighbor Search. Similarity-Preserving Binary Hashing. Why binary codes?

659 views • 34 slides

CSCE555 Bioinformatics

CSCE555 Bioinformatics. Lecture 11 Promoter Predication Meeting: MW 4:00PM-5:15PM SWGN2A21 Instructor: Dr. Jianjun Hu Course page: http://www.scigen.org/csce555. HAPPY CHINESE NEW YEAR. University of South Carolina Department of Computer Science and Engineering 2008 www.cse.sc.edu.

396 views • 34 slides

Meta-Learning : towards universal learning paradigms

Meta-Learning : towards universal learning paradigms. W?odzis?aw Duch Department of Informatics, Nicolaus Copernicus University , Toru ? , Poland Google: W. Duch UoT 7/2010. Toru ?. Norbert Tomek Marek Krzysztof. Copernicus. Nicolaus Copernicus: born in 1472.

512 views • 32 slides

Extending PRIX for Similarity-based XML Query

Extending PRIX for Similarity-based XML Query. Group Members: Yan Qi, Jicheng Zhao, Dan Situ, Ning Liao. Agenda. System Architecture Introduction Semantic-based Similarity Search Query Expansion Semantic Similarity Computation Structural-based Similarity Search Adapting PRIX algorithm

382 views • 26 slides

Similarity based deduplication

Similarity based deduplication. By: Lior Aronovich, Ron Asher, Eitan Bachmat, Haim Bitner, Michael Hirsch , Tomi Klein. Deduplication. There is a lot of redundancy in stored data, especially in backup data. Deduplication aims to store only the differences between different versions.

247 views • 22 slides

Graph-based Recommendation on Social Networks (IEEE2010 International Asia-Pacific Web Conference)

Graph-based Recommendation on Social Networks (IEEE2010 International Asia-Pacific Web Conference). School of Electronics Engineering and Computer Science Peking University Beijing, P.R. China Ziqi Wang, Yuwei Tan, Ming Zhang. Recommender Algorithm. Content-based recommendation

336 views • 17 slides