publication . Doctoral thesis . 2012

Randomized Algorithms for Scalable Machine Learning

Kleiner, Ariel Jacob;
Open Access English
  • Published: 01 Jan 2012
  • Publisher: eScholarship, University of California
Abstract
Many existing procedures in machine learning and statistics are computationally intractable in the setting of large-scale data. As a result, the advent of rapidly increasing dataset sizes, which should be a boon yielding improved statistical performance, instead severely blunts the usefulness of a variety of existing inferential methods. In this work, we use randomness to ameliorate this lack of scalability by reducing complex, computationally difficult inferential problems to larger sets of significantly smaller and more tractable subproblems. This approach allows us to devise algorithms which are both more efficient and more amenable to use of parallel and ...
Subjects
free text keywords: Computer science
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