Tracking Moving Agents via Inexact Online Gradient Descent Algorithm
نویسندگان
چکیده
منابع مشابه
Online gradient descent learning algorithm†
This paper considers the least-square online gradient descent algorithm in a reproducing kernel Hilbert space (RKHS) without an explicit regularization term. We present a novel capacity independent approach to derive error bounds and convergence results for this algorithm. The essential element in our analysis is the interplay between the generalization error and a weighted cumulative error whi...
متن کاملAdaptive Online Gradient Descent
We study the rates of growth of the regret in online convex optimization. First, we show that a simple extension of the algorithm of Hazan et al eliminates the need for a priori knowledge of the lower bound on the second derivatives of the observed functions. We then provide an algorithm, Adaptive Online Gradient Descent, which interpolates between the results of Zinkevich for linear functions ...
متن کاملAccelerating Stochastic Gradient Descent via Online Learning to Sample
Stochastic Gradient Descent (SGD) is one of the most widely used techniques for online optimization in machine learning. In this work, we accelerate SGD by adaptively learning how to sample the most useful training examples at each time step. First, we show that SGD can be used to learn the best possible sampling distribution of an importance sampling estimator. Second, we show that the samplin...
متن کاملLearning by Online Gradient Descent
We study online gradient{descent learning in multilayer networks analytically and numerically. The training is based on randomly drawn inputs and their corresponding outputs as deened by a target rule. In the thermo-dynamic limit we derive deterministic diierential equations for the order parameters of the problem which allow an exact calculation of the evolution of the generalization error. Fi...
متن کاملOnline Gradient Descent Learning Algorithms
This paper considers the least-square online gradient descent algorithm in a reproducing kernel Hilbert space (RKHS) without explicit regularization. We present a novel capacity independent approach to derive error bounds and convergence results for this algorithm. We show that, although the algorithm does not involve an explicit RKHS regularization term, choosing the step sizes appropriately c...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Journal of Selected Topics in Signal Processing
سال: 2018
ISSN: 1932-4553,1941-0484
DOI: 10.1109/jstsp.2018.2797423