'K points' diaporamas de présentation

3/23

HW 3 due on Thu 3/25 Midterm on Tu 3/30 Project 2 due on 4/6. 3/23. Agenda: Engineering Issues (Crawling; Connection Server; Distributed Indexing; Map-Reduce) Text Clustering (followed by Text Classification). Engineering Issues. Crawling Distributed Index Generation

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Group Key Management Scheme for Simultaneous Multiple Groups with Overlapped Membership

Group Key Management Scheme for Simultaneous Multiple Groups with Overlapped Membership. Andrew Moore 9/27/2011. Overview. Review of Group Communication Background Information Scheme Definitions Protocol Discussion Example Results Conclusion. Group Communication.

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K-means clustering

CZ5225: Modeling and Simulation in Biology Lecture 4: Clustering Analysis for Microarray Data II Prof. Chen Yu Zong Tel: 6874-6877 Email: yzchen@cz3.nus.edu.sg http://xin.cz3.nus.edu.sg Room 07-24, level 7, SOC1, NUS. K-means clustering. This method differs from the hierarchical clustering

1.12k views • 42 slides

Large-Scale Optimization in VLSI CAD

Large-Scale Optimization in VLSI CAD. Igor Markov http://www.eecs.umich.edu/~imarkov. Goals/Outline of the Talk. Give a general idea about the field success stories and applications potential for cross-pollination What drives the field Reusable Intellectual Property in CAD

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Latent Dirichlet Allocation

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On

On. Locally Decodable Codes. Self Correctable Codes. and. t-private PIR. Omer Barkol, Yuval Ishai and Enav Weinreb Technion, Israel. x ? {0,1} n. Private Information Retrieval. On. [CGKS95]. Locally Decodable Codes. Self Correctable Codes. Client. q. Server. and. A(q,x). i ?[n].

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CMPS 3120: Computational Geometry Spring 2013

CMPS 3120: Computational Geometry Spring 2013. Orthogonal Range Searching Carola Wenk. Orthogonal range searching. Input: n points in d dimensions E.g., representing a database of n records each with d numeric fields. Query: Axis-aligned box (in 2D, a rectangle)

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The Idea in a Nutshell

The Idea in a Nutshell . Classify a new item based on the classes of its k nearest neighbors. Sensitivity to k distance metric how the k points contribute to the decision . Computational Challenges. Distance metric needs to be tuned (e.g., parameter scaling).

124 views • 3 slides