Results 51 to 60 of about 1,338,246 (278)
Perbandingan Teknik Klasifikasi Dalam Data Mining Untuk Bank Direct Marketing
Jurnal Teknologi Informasi dan Ilmu Komputer, 2018 Klasifikasi merupakan teknik dalam data mining untuk mengelompokkan data berdasarkan keterikatan data terhadap data sampel. Pada penelitian ini, kami melakukan perbandingan 9 teknik klasifikasi untuk mengklasifikasi respon pelanggan pada dataset Bank ...
Irvi Oktanisa, Ahmad Afif Supianto
doaj +1 more source
Counterexamples for Noise Models of Stochastic Gradients
Examples and Counterexamples, 2023 Stochastic Gradient Descent (SGD) is a widely used, foundational algorithm in data science and machine learning. As a result, analyses of SGD abound making use of a variety of assumptions, especially on the noise behavior of the stochastic gradients ...
Vivak Patel
doaj +1 more source
A Fixed-Point of View on Gradient Methods for Big Data
Frontiers in Applied Mathematics and Statistics, 2017 Interpreting gradient methods as fixed-point iterations, we provide a detailed analysis of those methods for minimizing convex objective functions. Due to their conceptual and algorithmic simplicity, gradient methods are widely used in machine learning ...
Alexander Jung
doaj +1 more source
Adaptive Optical Closed-Loop Control Based on the Single-Dimensional Perturbation Descent Algorithm
Sensors, 2023 Modal-free optimization algorithms do not require specific mathematical models, and they, along with their other benefits, have great application potential in adaptive optics.
Bo Chen +4 more
doaj +1 more source
Stochastic Gradient Descent for Risk Optimization
, 2020 This paper presents an approach for the use of stochastic gradient descent methods for the solution of risk optimization problems. The first challenge is to avoid the high-cost evaluation of the failure probability and its gradient at each iteration of ...
Lopez, Rafael Holdorf +7 more
core +1 more source
An efficient algorithm for data parallelism based on stochastic optimization
Alexandria Engineering Journal, 2022 Deep neural network models can achieve greater performance in numerous machine learning tasks by raising the depth of the model and the amount of training data samples.
Khalid Abdulaziz Alnowibet +3 more
doaj +1 more source
Distributed stochastic gradient descent for link prediction in signed social networks
EURASIP Journal on Advances in Signal Processing, 2019 This paper considers the link prediction problem defined over a signed social network, where the relationship between any two network users can be either positive (friends) or negative (foes).
Han Zhang, Gang Wu, Qing Ling
doaj +1 more source
SSRGD: Simple stochastic recursive gradient descent for escaping saddle points [PDF]
, 2019 We analyze stochastic gradient algorithms for optimizing nonconvex problems. In particular, our goal is to find local minima (second-order stationary points) instead of just finding first-order stationary points which may be some bad unstable saddle ...
Li, Zhize
core
Golgi enzymes are retrieved from the plasma membrane to the trans‐Golgi network
FEBS Letters, EarlyView.Golgi enzymes are traditionally considered resident proteins retained within the Golgi apparatus. Here, we demonstrate that a subset transiently reaches the cell surface and is subsequently retrieved to the trans‐Golgi network via retrograde transport. Using a nanobody‐based toolkit, we uncover a dynamic trafficking cycle of several Golgi enzymes.
Dominik P. Buser, Tina Junne
wiley +1 more source
Adaptive Step Sizes for Stochastic Gradient Descent
, 2023 In this thesis, we first lay some theoretical groundwork before motivating and discussing the stochastic gradient descent method along with its variations. We then analyze some popular step size strategies with a focus on the stochastic Polyak step size,
Karakoc, Dylan
core +1 more source