PPT-Scalable Visual Instance Mining with Threads of Features

Wei Zhang Hongzhi Li ChongWah Ngo ShihFu Chang City Univeristy of Hong Kong Columbia University 1 Visual Instance Mining visual instance a specific visual entity object car apple flower. SIDEBAR HEAD KEY FEATURES x Easy to install configure and administer x High availability for the load balancer and for the back end servers x High performance through SSLTLS termination content caching and HTTP compression x High throughput with low It provides financ ial managers the ability to rapidly consolidate and report financial results meet global regulatory requirements reduce the cost of compliance and del iver confidence in the numbers Meet Todays Stringent Reporting Regulations Many Howe ver as the size of the aw data incr eases par allel data mining algo rithms ar becoming necessity In this paper we pr esent runtime support system that was designed to allow the ef 64257 cient implementation of datamining algorithms on heter o Clustering and Bag of . Words Representations. Many slides adapted from. Svetlana . Lazebnik. , . Fei. -Fei. Li, Rob Fergus, and Antonio . Torralba. Announcements. HW1 due . Thurs. , Sept . 27 @ 12pm. Recognition tasks. Machine learning approach: training, testing, generalization. Example classifiers. Nearest neighbor. Linear classifiers. Image features. Spatial support:. Pixel or local patch. Segmentation region. Upgrading. to SQL Server 2014. Michał Sadowski. PLSSUG Kraków. michal.sadowski@plssug.org.pl. @. SadowskiMichal. Few. . words. . about. me. Leader of PLSSUG . Kraków. DBA of financial applications . Finish attention lectures this week. No class Tuesday next week. What should you do instead?. Start memory Thursday next week. Read Oliver Sacks – The Lost Mariner for Thursday (26. th. ). Read Elizabeth Loftus (For the following week). 1. Nearest Neighbor Learning. Classify based on local similarity. Ranges from simple . nearest neighbor . to case-based and analogical reasoning. Use local information near the current query instance to decide the classification of that instance. Oman. Hilal al-. Busaidi. Public Authority for Mining. Outlines. Mining History in Oman. Presence of Minerals . in Oman. Mining . Sector in Om. an. Mining . Investment . Opportunities. . Mining History in Oman. Matt Welsh, David Culler, and Eric Brewer. Computer Science Division. University of California, Berkeley. Presented By:. Linh Nguyen. Agenda . Motivations and . background. Concurrencies. SEDA. Goals. www.tatalab.ca. Reminder: extra credit experiments . www.tatalab.ca. Upcoming Reading. Vokey. and Read – Subliminal Messages . Tuesday next week. Visual Search: finding a single item in a cluttered visual scene. Outline. The importance of instance selection. Rough set theory. Fuzzy-rough sets. Fuzzy-rough instance selection. Experimentation. Conclusion. Knowledge discovery. The problem of too much data. Requires storage. S. OCIAL. M. EDIA. M. INING. Dear instructors/users of these slides: . Please feel free to include these slides in your own material, or modify them as you see fit. If you decide to incorporate these slides into your presentations, please include the following note:. two dimensional. , . scaled. . down . representation. of . selected geospatial information . within a . ‘. geographical . area of interest. .’ . The . size and type of the . display medium. as well as the map’s .