4 / 18
Page 53
Journal of Computer Science & Systems Biology | ISSN: 0974-7230 | Volume: 11
&
Biostatistics and Bioinformatics
Big Data Analytics & Data Mining
7
th
International Conference on
7
th
International Conference on
September 26-27, 2018 | Chicago, USA
Adaptive sequential learning
Venugopal V Veeravalli
University of Illinois at Urbana-Champaign, USA
A
framework is introduced for learning a sequence of slowly changing tasks, where the parameters of the learning algorithm are
obtained by minimizing a loss function to the desired accuracy using optimization algorithms such as Stochastic Gradient
Descent (SGD). The tasks are assumed to change slowly in the sense that the optimum values of the learning algorithm parameters
change at a bounded rate. An adaptive sequential learning algorithm is developed to efciently solve such a slowly varying sequence
of tasks. The key idea behind the approach, which distinguishes it from existing methods for online optimization, involves using a
probably efcient estimator for the change in minimizer in conjunction with the optimization at each stage. This estimator allows for
an accurate tracking of the minimizer over time, thereby adapting the algorithm to allow it to use the fewest number of samples at
each stage. Experiments with synthetic and real data sets are presented that validate the theoretical results. Extensions to incorporate
possible abrupt changes and active learning are also discussed..
vvv@illinois.eduJ Comput Sci Syst Biol 2018, Volume: 11
DOI: 10.4172/0974-7230-C1-021