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Convergence of Momentum-Based Heavy Ball Method with Batch Updating and/or Approximate Gradients
In this paper, we study the well-known "Heavy Ball" method for convex and nonconvex optimization introduced by Polyak in 1964, and establish its convergence under a variety of situations.
Evince the artifacts of Spoof Speech by blending Vocal Tract and Voice Source Features
We achieve 99. 58\% in algorithm classification accuracy.
Convergence of Batch Updating Methods with Approximate Gradients and/or Noisy Measurements: Theory and Computational Results
The framework encompasses all the currently used batch updating approaches, and is applicable to nonconvex as well as convex functions.
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