PDF-Approximate Clustering without the Approximation MariaFlorina Balcan Avrim Blum

This quest for better approximation algorithms is further fueled by the implicit hope that these better approximation also yield more accurate clusterings Eg for many prob lems such as clustering proteins by function or clustering images by subject. cmuedu School of Computer Science Carnegie Mellon University Pittsburgh PA 152133891 Alina Beygelzimer beygelusibmcom IBM T J Watson Research Center Hawthorne NY 10532 John Langford jltticorg Toyota Technological Institute at Chicago Chicago IL 60637 cmuedu Avrim Blum Carnegie Mellon University avrimcscmuedu Or Sheffet Carnegie Mellon University osheffetcscmuedu Abstract Given some arbitrary distribution over and arbitrary target function the problem of agnostic learning of disjunctions is to ac Hardware: Challenges and Opportunities. Author. : Bingsheng He. (Nanyang Technological University, Singapore) . Speaker. : . Jiong . He . (Nanyang Technological University, Singapore. ). 1. What is Approximate Hardware?. waves. Lauren Blum. Meredith et al. (2004). Lauren Blum - University of Colorado - GEM 2013 waves tutorial. Wave Particle Interactions. Cyclotron . Resonance:. Shprits. et al. (2006). Lauren Blum - University of Colorado - GEM 2013 waves tutorial. Andrew B. Kahng, . Seokhyeong Kang . VLSI CAD LABORATORY, . UC. San Diego. 49. th. Design Automation Conference. June 6. th. , 2012. Outline. Background and Motivation. Accuracy Configurable Adder Design. Grigory. . Yaroslavtsev. http://grigory.us. . With . Shuchi. . Chawla. (University of Wisconsin, Madison),. Konstantin . Makarychev. (Microsoft Research),. Tselil. Schramm (University of California, Berkeley). Ulya. . R. . Karpuzcu. ukarpuzc@umn.edu. . 12/01/2015. Outline. Background. Pitfalls & Fallacies. Practical Guidelines. 2. 12/01/2015. On Quantification of Accuracy Loss in Approximate Computing. issue in . computing a representative simplicial complex. . Mapper does . not place any conditions on the clustering . algorithm. Thus . any domain-specific clustering algorithm can . be used.. We . What is clustering?. Why would we want to cluster?. How would you determine clusters?. How can you do this efficiently?. K-means Clustering. Strengths. Simple iterative method. User provides “K”. Unsupervised . learning. Seeks to organize data . into . “reasonable” . groups. Often based . on some similarity (or distance) measure defined over data . elements. Quantitative characterization may include. Lecture outline. Distance/Similarity between data objects. Data objects as geometric data points. Clustering problems and algorithms . K-means. K-median. K-center. What is clustering?. A . grouping. of data objects such that the objects . Tamara Berg. CS 590-133 Artificial Intelligence. Many slides throughout the course adapted from Svetlana . Lazebnik. , Dan Klein, Stuart Russell, Andrew Moore, Percy Liang, Luke . Zettlemoyer. , Rob . Produces a set of . nested clusters . organized as a hierarchical tree. Can be visualized as a . dendrogram. A . tree-like . diagram that records the sequences of merges or splits. Strengths of Hierarchical Clustering. Randomization tests. Cluster Validity . All clustering algorithms provided with a set of points output a clustering. How . to evaluate the “goodness” of the resulting clusters?. Tricky because .