'Similarity function' diaporamas de présentation

Supervised Learning Artificial Neural Networks Support Vector Machines

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Recommender Systems

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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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Motivation and Outline

Motivation and Outline. Background Definitions, etc. The Problem 100,000+ pages The Solution Ranking docs Vector space Extensions Relevance feedback, clustering, query expansion, etc. Motivation. IR: representation, storage, organization of, and access to information items

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Segmentation using eigenvectors

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Text Clustering

Text Clustering. Clustering. Partition unlabeled examples into disjoint subsets of clusters , such that: Examples within a cluster are very similar Examples in different clusters are very different Discover new categories in an unsupervised manner (no sample category labels provided).

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Slide 0

Lecture 1 MATHEMATICS OF THE BRAIN with an emphasis on the problem of a universal learning computer (ULC) and a universal learning robot (ULR) Victor Eliashberg Consulting professor, Stanford University, Department of Electrical Engineering. Slide 0.

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Support Vector Machine & Its Applications

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Features, Kernels, and Similarity functions

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KLASTERISASI

KLASTERISASI. TIM PENGAJAR. CLUSTERING Basic Concepts

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Toward the Next generation of Recommender systems

Toward the Next generation of Recommender systems. 2008. 11.05 IEEE Transactions on Knowledge and Data Engineering Volume 17 , Issue 6 (June 2005) Written by Gediminas Adomavicius , Alexander Tuzhilin Summarized by Gihyun Gong. About paper.

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Similarity Estimation Techniques from Rounding Algorithms

Similarity Estimation Techniques from Rounding Algorithms. Moses Charikar Princeton University. Compact sketches for estimating similarity. Collection of objects, e.g. mathematical representation of documents, images. Implicit similarity/distance function.

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Kernel Methods for Relation Extraction

Kernel Methods for Relation Extraction. ???. Journal of Machine Learning Research 3 (2003) 1083-1106. 2003 Dmitry Zelenko , Chinatsu Aone and Anthony Richardella. Extract What Relation. person-affiliation(organization) John Smith is the chief scientist of the Hardcom Corporation

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Improving Similarity Measures for Short Segments of Text

Improving Similarity Measures for Short Segments of Text. Scott Wen -tau Yih & Chris Meek Microsoft Research. query mariners. Query Suggestion. How similar are they? mariners vs. seattle mariners mariners vs. 1st mariner bank. Keyword Expansion for Online Ads. Chocolate Cigarettes.

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Improving Similarity Measures for Short Segments of Text

Improving Similarity Measures for Short Segments of Text. Scott Wen -tau Yih & Chris Meek Microsoft Research. query mariners. Query Suggestion. How similar are they? mariners vs. seattle mariners mariners vs. 1st mariner bank. Keyword Expansion for Online Ads. Chocolate Cigarettes.

314 views • 19 slides

Improving Similarity Measures for Short Segments of Text

Improving Similarity Measures for Short Segments of Text. Scott Wen -tau Yih & Chris Meek Microsoft Research. query mariners. Query Suggestion. How similar are they? mariners vs. seattle mariners mariners vs. 1st mariner bank. Keyword Expansion for Online Ads. Chocolate Cigarettes.

276 views • 19 slides

Project 1

Project 1. Mei-Chen Yeh 03/13/2012. Searching landmark photos. Data (around 720 images) 90 (18 * 5) folders, each has 8 images. Chiang Kai- Shek Memorial Hall . Yeh Liu. NTNU Gong-Guan campus. Kiyomizu-dera in Kyoto. National Palace Museum. Marina Bay Sands in Singapore.

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Periodic Limb movements

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Stereo Mix Source Identification and Separation

Stereo Mix Source Identification and Separation. Based on work done by Carlos Avendano By: Jeff England EE 6820. How a Stereo Mix is Created. Each Source Recorded Independently Sources are then mixed together with different panning indexes ? ij is the amplitude-panning coefficient

169 views • 8 slides