'Knn' presentation slideshows

CS145 Discussion Week 5

CS145 Discussion Week 5. Junheng, Shengming, Yunsheng 11/2/2018. Roadmap. Announcement K-NN Similarity Metrics ROC K-Means Homework 3 Course Project Midterm Report. Announcement. Homework 3 out Deadline: 11/09 Friday 11:59 pm KNN(30%) and Neural Network(70%)

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Efficient Probabilistic Reverse Nearest Neighbor Query Processing on Uncertain Data

Efficient Probabilistic Reverse Nearest Neighbor Query Processing on Uncertain Data. Thomas Bernecker, Tobias Emrich , Hans-Peter Kriegel, Matthias Renz, Stefan Zankl and Andreas Zuefle. Outline. Background Uncertain Data Model Reverse k-nearest neighbour queries

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Reverse Spatial and Textual k Nearest Neighbor Search

Reverse Spatial and Textual k Nearest Neighbor Search. Athens, Greece, 2011. Outline. Motivation & Problem Statement Related Work RSTkNN Search Strategy Experiments Conclusion. 1. Motivation. clothes. food. clothes. clothes. sports. food. clothes.

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Reverse Spatial and Textual k Nearest Neighbor Search

Reverse Spatial and Textual k Nearest Neighbor Search. Outline. Motivation & Problem Statement Related Work RSTkNN Search Strategy Experiments Conclusion. 1. Motivation. If add a new shop at Q, which shops will be influenced? Influence facts Spatial Distance Results: D, F

388 views • 27 slides

Reverse Spatial and Textual k Nearest Neighbor Search

Reverse Spatial and Textual k Nearest Neighbor Search. Athens, Greece, 2011. Outline. Motivation & Problem Statement Related Work RSTkNN Search Strategy Experiments Conclusion. 1. Motivation. clothes. food. clothes. clothes. sports. food. clothes.

284 views • 27 slides

Hanaa M. Hussain, Khaled Benkrid School of Engineering Edinburgh University, Edinburgh

School of Engineering University of Guelph. An Adaptive Implementation of a Dynamically Reconfigurable K-Nearest Neighbor Classifier On FPGA (2012). Hanaa M. Hussain, Khaled Benkrid School of Engineering Edinburgh University, Edinburgh Scotland, U.K. { h.hussain , k.benkrid }@ed.ac.uk.

483 views • 26 slides

DIKNN: An Itinerary-based KNN Query Processing Algorithm for Mobile Sensor Networks

DIKNN: An Itinerary-based KNN Query Processing Algorithm for Mobile Sensor Networks. Shan-Hung Wu Kun-Ta Chuang Chung-Min Chen Ming-Syan Chen. Outline. Introduction Related Work Design of DIKNN KNN Boundary Estimation Performance Evaluation Conclusion. Introduction.

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Musical Genre Categorization Using Support Vector Machines

Musical Genre Categorization Using Support Vector Machines. Shu Wang. Outline. Motivation Dataset Feature Extraction Automatic Classification Conclusion. Motivation. Music Information Retrieval. Music Genres. http://www.flickr.com/photos/elbewerk/2845839180/lightbox/. Dataset.

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K Nearest Neighbors

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?? 4 ? KNN ??

?? 4 ? KNN ??. ????. ??????? KNN ??? ?????? C ? C++ ???????? 150 ???????? 100 ???????? K=7 ???? 50 ????????. ????????????.

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Approximate NN queries on Streams with Guaranteed Error/performance Bounds

Approximate NN queries on Streams with Guaranteed Error/performance Bounds. Nick Koudas @ AT&T labs-research Beng Chin Ooi , Kian-Lee Tan , Rui Zhang @ National University of Singapore. Problem. Problem: kNN search. Environment: data stream (one scan; memory constraint).

198 views • 10 slides

Approximate NN queries on Streams with Guaranteed Error/performance Bounds

Approximate NN queries on Streams with Guaranteed Error/performance Bounds. Nick Koudas @ AT&T labs-research Beng Chin Ooi , Kian-Lee Tan , Rui Zhang @ National University of Singapore. Problem. Problem: kNN search. Environment: data stream (one scan; memory constraint).

102 views • 10 slides

Data Mining

Data Mining. extracting knowledge from a large amount of data. Useful Information (sometimes 1 bit: Y/N). More data volume = less information. Data Mining. Raw data. Information Pyramid.

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Lazy learning vs. eager learning

Lazy learning vs. eager learning. Processing is delayed until a new instance must be classified Pros: Classification hypothesis is developed locally for each instance to be classified Cons: Running time (no model is built, so each classification actually builds a local model from scratch).

501 views • 9 slides