J
John Platt
Researcher at Microsoft
Publications - 369
Citations - 66980
John Platt is an academic researcher from Microsoft. The author has contributed to research in topics: Support vector machine & Artificial neural network. The author has an hindex of 83, co-authored 369 publications receiving 60242 citations. Previous affiliations of John Platt include Google & California Institute of Technology.
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Fast training of support vector machines using sequential minimal optimization
TL;DR: In this article, the authors proposed a new algorithm for training Support Vector Machines (SVM) called SMO (Sequential Minimal Optimization), which breaks this large QP problem into a series of smallest possible QP problems.
Journal ArticleDOI
Supplementary information for "Quantum supremacy using a programmable superconducting processor"
Frank Arute,Kunal Arya,Ryan Babbush,Dave Bacon,Joseph C. Bardin,Rami Barends,Rupak Biswas,Sergio Boixo,Fernando G. S. L. Brandão,David A. Buell,B. Burkett,Yu Chen,Zijun Chen,Ben Chiaro,Roberto Collins,William Courtney,Andrew Dunsworth,Edward Farhi,Brooks Foxen,Austin G. Fowler,Craig Gidney,Marissa Giustina,R. Graff,Keith Guerin,Steve Habegger,Matthew P. Harrigan,Michael J. Hartmann,Alan Ho,Markus R. Hoffmann,Trent Huang,Travis S. Humble,Sergei V. Isakov,Evan Jeffrey,Zhang Jiang,Dvir Kafri,Kostyantyn Kechedzhi,Julian Kelly,Paul V. Klimov,Sergey Knysh,Alexander N. Korotkov,Fedor Kostritsa,David Landhuis,Mike Lindmark,Erik Lucero,Dmitry I. Lyakh,Salvatore Mandrà,Jarrod R. McClean,Matt McEwen,Anthony Megrant,Xiao Mi,Kristel Michielsen,Masoud Mohseni,Josh Mutus,Ofer Naaman,Matthew Neeley,Charles Neill,Murphy Yuezhen Niu,Eric Ostby,Andre Petukhov,John Platt,Chris Quintana,Eleanor Rieffel,Pedram Roushan,Nicholas C. Rubin,Daniel Sank,Kevin J. Satzinger,Vadim Smelyanskiy,Kevin Sung,Matthew D. Trevithick,Amit Vainsencher,Benjamin Villalonga,Theodore White,Z. Jamie Yao,Ping Yeh,Adam Zalcman,Hartmut Neven,John M. Martinis +76 more
TL;DR: In this paper, an updated version of supplementary information to accompany "Quantum supremacy using a programmable superconducting processor", an article published in the October 24, 2019 issue of Nature, is presented.
Williamson, estimating the support of a high-dimensional distribution
TL;DR: The algorithm is a natural extension of the support vector algorithm to the case of unlabeled data by carrying out sequential optimization over pairs of input patterns and providing a theoretical analysis of the statistical performance of the algorithm.
Journal ArticleDOI
Estimating the Support of a High-Dimensional Distribution
TL;DR: In this paper, the authors propose a method to estimate a function f that is positive on S and negative on the complement of S. The functional form of f is given by a kernel expansion in terms of a potentially small subset of the training data; it is regularized by controlling the length of the weight vector in an associated feature space.