PPT-Collaborative Filtering

Agenda Collaborative Filtering CF Pure CF approaches Userbased nearestneighbor The Pearson Correlation similarity measure Memorybased and modelbased approaches Itembased nearestneighbor. F01943024. Reference. Yang, . Qingxiong. . "Recursive bilateral filtering." . ECCV . 2012. .. Deriche. , . Rachid. . "Recursively . implementating. the Gaussian and its derivatives." . ICIP 1993.. 2. An Adaptive Framework for Similarity Join and Search. Jiannan. Wang. . (Tsinghua University). Guoliang. . Li (Tsinghua . University). Jianhua. . Feng. (Tsinghua University). Data Integration. Data Cleaning. Information Retrieval in Practice. All slides ©Addison Wesley, 2008. Social Search. Social search . Communities. of users . actively participating. in the search process. Goes beyond classical search tasks. Stacy Morgan. LIS 600. UNC Greensboro. 23 October 2013. The Setting. How is internet used in the . school library?. How is internet used in the school library?. Today’s students are “digital natives”, born into a culture and lifestyle where technology immersion is the norm (. Bree Collaborative Meeting. March 27, 2013. Objectives for Today. Present general plan for retreat. Review tentative agenda items . Get feedback from the group to guide preparation. 2. Overall Vision for Retreat. July 15, 2014. Martin Center for Professional Development. Nashville, TN. Welcome and Introduction. Margie Johnson. Coordinator of Business Intelligence, MNPS. . MNPS Data Use Research Alliance goals. Presentation by. Helen. Haynes. International. Partnerships Team Leader. January 2017. Collaborative Provision – International Partners. Programmes. delivered at our International . partner institutions as part of collaborative . Prasun Dewan. Department of Computer Science. University of North Carolina at Chapel Hill. dewan@cs.unc.edu. COLLABORATIVE APPLICATION. U1. U2. U3. Application. User inputs “draw red circle”. Coupling. Charles Heckscher. August, . 2017. 1. CRAFT / AUTONOMOUS PROFESSIONAL NETWORKS. Customization and personal relations. Challenge: to increase scale of production and scope of distribution. 1900-. 1980. Present the model’s components in order to move forward together to the core implementation project team. Meeting Process: Brief overview of the model points with highlights from key pieces. . Agenda. Fouhey. .. Let’s Take An Image. Let’s Fix Things. Slide Credit: D. Lowe. We have noise in our image. Let’s replace each pixel with a . weighted. average of its neighborhood. Weights are . filter kernel. Outline. Recap. SVD . vs. PCA. Collaborative filtering. aka Social recommendation. k-NN CF methods. classification. CF via MF. MF . vs. SGD . vs. ….. Dimensionality Reduction. and Principle Components Analysis: Recap. An introduction. CS578-Digital speech signal processing. Invited lecture. On the (Glottal) Inverse Filtering of Speech Signals. Introduction. Inverse Filtering Techniques. Conclusions. Introduction. On the (Glottal) Inverse Filtering of Speech Signals. Matthew Heintzelman. EECS 800 SAR Study Project . ‹#›. . Background:. Typical SAR image formation . algorithms. produce relatively high sidelobes (fast-time and slow-time) that . contribute. to image speckle and can mask scatterers with a low RCS..