• No results found

Stochastic Gradient Descent Based Support Vector Machines Training Optimization on Big Data and HPC Frameworks

N/A
N/A
Protected

Academic year: 2020

Share "Stochastic Gradient Descent Based Support Vector Machines Training Optimization on Big Data and HPC Frameworks"

Copied!
10
0
0

Loading.... (view fulltext now)

Full text

Loading

Figure

FIGURE 1 SGD SVM Algorithm
FIGURE 3 C++ Single Node Experiments
FIGURE 10 Big Data Stack vs HPC Benchmark on Dis-tributed Ensemble SVM

References

Related documents

Maps with the overlay method were combined together and using the multi-criteria decision-making (MCDM) techniques, the best place for parks in the case study area was

However, enhancing engineering capabilities and technical excellence, although readily recognized as key motivations, is not often seen in the list of motivations for the

While work on automatic still image retargeting inspired and informs our approach, the video retargeting problem is more challenging for a number of reasons including: determining

Scope definition and many of the practices defined in the PMBOK knowledge area of Project Time Management are done as part of iteration planning, where features are elaborated,

Dougy Center provides more than 1,300 grief support groups each year for children, teens, young adults, and their family members at no expense to the family. These groups

Keywords: Demand, productivity, markups, production function estimation, export status, firm size JEL codes: D24; L11; L25; F14.. This paper was produced as part of the Centre’s

As a high school English teacher, I have to prepare my students for all levels of college writing, not just English class and “different colleges in the same area have different

Hence, almost all existing work of spectrum slices scheduling in wireless network virtualization followed the same line and developed spectrum schedulers which partition and