The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Abstract Details
Activity Number:
|
288
|
Type:
|
Topic Contributed
|
Date/Time:
|
Tuesday, August 2, 2011 : 8:30 AM to 10:20 AM
|
Sponsor:
|
Section on Statistical Computing
|
Abstract - #301869 |
Title:
|
Algorithms For Machine Learning On Massive Datasets
|
Author(s):
|
Alexander Gray*+
|
Companies:
|
Georgia Institute of Technology
|
Address:
|
Klaus Advanced Computing Building, Atlanta, GA, 30332,
|
Keywords:
|
massive data ;
algorithms ;
computational methods ;
machine learning
|
Abstract:
|
I'll describe algorithms and data structures for allowing the most powerful machine learning methods, which often scale quadratically or even cubically with the number of data points, to be performed many orders of magnitude faster than naive implementations. Such techniques can make previously impossible statistical analyses tractable on the scale of entire sky surveys. I will touch on scalable algorithms we have developed for nearest-neighbors, kernel density estimation, nonparametric Bayes classification, principal component analysis, local linear regression, hidden Markov models, k-means, manifold learning, support vector machines, and n-point correlation functions, among others. In addition to techniques inspired by computational geometry, fast multipole methods, and Monte Carlo integration, we employ a distributed framework which can be thought of as a higher-order version of Google's MapReduce. Our algorithms have enabled several first-of-a-kind large-scale analyses of astronomy data, networking data, biomedical data, and others.
|
The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
Back to the full JSM 2011 program
|
2011 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please contact the Education Department.