• No results found

SGD-QN: Careful Quasi-Newton Stochastic Gradient Descent

N/A
N/A
Protected

Academic year: 2020

Share "SGD-QN: Careful Quasi-Newton Stochastic Gradient Descent"

Copied!
18
0
0

Loading.... (view fulltext now)

Full text

Loading

Figure

Table 2: The regularization term in the primal cost can be viewed as an additional training examplewith an arbitrarily chosen frequency and a specific loss function.
Figure 1: Detailed pseudo-codes of the SGD and SVMSGD2 algorithms.
Table 3: Costs of various operations on a vector of dimension d with s nonzero coefficients.
Figure 2 compares the SVMSGD2iteration demands less than twice the time of a first-order SGD iteration.efficients during the next iteration
+6

References

Related documents

Model 2 in which firms with medium market competitive density are compared to the ones with high market competitive density has shown that usage of learning and growth measures

I find no evidence of any effects of statewide student achievement data on education policy preferences such as increasing overall spending levels, increasing teacher

Chapter 4 presents a solution to surgical case assignment problem (SCAP), it introduces a stochastic model for the operating block planning and scheduling; a model that incorporates

In this study, orientation and location finder services for indoor navigation will be done by using accelerometer, compass and camera that have been already included in the phones

The ideal of self-r eliance cer tainly does not exclude this other ideal of shar ing, of the g iv e-and-tak e, whether spontaneous or institutionalized, that

Gender, mother language, performances of the English language at the GCE (O/L) examination and the general English at the GCE (A/L) examination, the stream of study of the

In batch learning, stability together with existence and uniqueness of the solution corresponds to well-posedness of Empirical Risk Minimization (ERM) methods; recently, it was