'Similarity functions' presentation slideshows

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Searching and Integrating Information on the Web

Searching and Integrating Information on the Web

Searching and Integrating Information on the Web. Seminar 3: Data Cleansing Professor Chen Li UC Irvine. Paper readings. Efficient merge and purge: Hernandez and Stolfo, SIGMOD 1995 Approximate String Joins in a Database (Almost) for Free, Gravano et al, VLDB 2001,

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832 views • 73 slides
Searching and Integrating Information on the Web

Searching and Integrating Information on the Web

Searching and Integrating Information on the Web. Seminar 3: Data Cleansing Professor Chen Li UC Irvine. Paper readings. Efficient merge and purge: Hernandez and Stolfo, SIGMOD 1995 Approximate String Joins in a Database (Almost) for Free, Gravano et al, VLDB 2001,

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734 views • 73 slides
On a Theory of Similarity functions for Learning and Clustering

On a Theory of Similarity functions for Learning and Clustering

On a Theory of Similarity functions for Learning and Clustering. Avrim Blum Carnegie Mellon University This talk is based on work joint with Nina Balcan, Nati Srebro and Santosh Vempala. Theory and Practice of Computational Learning, 2009. 2-minute version.

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548 views • 44 slides
CS 188: Artificial Intelligence

CS 188: Artificial Intelligence

CS 188: Artificial Intelligence. Kernels and Clustering. Instructors: Dan Klein and Pieter Abbeel --- University of California, Berkeley

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500 views • 41 slides
Record Linkage/ Duplicate Elimination Sunita Sarawagi Sunita@iitb.ac

Record Linkage/ Duplicate Elimination Sunita Sarawagi Sunita@iitb.ac

Record Linkage/ Duplicate Elimination Sunita Sarawagi Sunita@iitb.ac.in. The de-duplication problem. Given a list of semi-structured records, find all records that refer to a same entity Example applications: Data warehousing: merging name/address lists Entity: Person Household

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456 views • 35 slides
A Theory of Learning and Clustering via Similarity Functions

A Theory of Learning and Clustering via Similarity Functions

A Theory of Learning and Clustering via Similarity Functions. Maria-Florina Balcan. Carnegie Mellon University. Joint work with Avrim Blum and Santosh Vempala. 09/17/2007. K( , ). 2-Minute Version. Generic classification problem:. learn to distinguish men from women.

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347 views • 34 slides
Learning with Similarity Functions

Learning with Similarity Functions

Learning with Similarity Functions. Maria-Florina Balcan & Avrim Blum CMU, CSD. Kernels and Similarity Functions. Kernels have become a powerful tool in ML. Useful in practice for dealing with many different kinds of data.

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367 views • 16 slides
Learning with Similarity Functions

Learning with Similarity Functions

Learning with Similarity Functions. Maria-Florina Balcan & Avrim Blum CMU, CSD. Kernels and Similarity Functions. Kernels have become a powerful tool in ML. Useful in practice for dealing with many different kinds of data.

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176 views • 16 slides

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