Results 51 to 60 of about 141,184 (170)
Text Sentiment Analysis Based on Hybrid Chi-square Statistic and Logistic Regression [PDF]
Jisuanji gongcheng, 2017 In text sentiment analysis,feature extraction method based on Chi-square statistic (CHI) is easy to ignore single text word frequency which leads to text feature accuary is low,a feature extraction method based on hybrid chi-square statistics is proposed.
LI Ping,DAI Yueming,WANG Yan
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The effective noise of stochastic gradient descent
Journal of Statistical Mechanics: Theory and Experiment, 2022 Abstract Stochastic gradient descent (SGD) is the workhorse algorithm of deep learning technology. At each step of the training phase, a mini batch of samples is drawn from the training dataset and the weights of the neural network are adjusted according to the performance on this specific subset of examples.
Mignacco, Francesca +1 more
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Fractional Stochastic Search Algorithms: Modelling Complex Systems via AI
Mathematics, 2023 The aim of this article is to establish a stochastic search algorithm for neural networks based on the fractional stochastic processes {BtH,t≥0} with the Hurst parameter H∈(0,1).
Bodo Herzog
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Regularized Dual Averaging Image Reconstruction for Full-Wave Ultrasound Computed Tomography
, 2017 Ultrasound computed tomography (USCT) holds great promise for breast cancer screening. Waveform inversion-based image reconstruction methods account for higher order diffraction effects and can produce high-resolution USCT images, but are computationally
Anastasio, Mark A. +4 more
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Fast Ridge Regression with Randomized Principal Component Analysis and Gradient Descent [PDF]
, 2014 We propose a new two stage algorithm LING for large scale regression problems. LING has the same risk as the well known Ridge Regression under the fixed design setting and can be computed much faster. Our experiments have shown that LING performs well in
Foster, Dean P., Lu, Yichao
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Optimising continuous microstructures: a comparison of gradient-based and stochastic methods [PDF]
, 2004 This work compares the use of a deterministic gradient based search with a stochastic genetic algorithm to optimise the geometry of a space frame structure.
Hanna, S., Haroun Mahdavi, S.
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Applied SciencesThe secondary use of electronic health records is essential for developing artificial intelligence-based clinical decision support systems. However, even after direct identifiers are removed, de-identified electronic health records remain vulnerable to ...
Jungwoo Lee, Kyu Hee Lee
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Компьютерная оптика, 2023 In this paper, we propose a natural gradient descent algorithm with momentum based on Dirichlet distributions to speed up the training of neural networks. This approach takes into account not only the direction of the gradients, but also the convexity of
R.I. Abdulkadirov, P.A. Lyakhov
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Stochastic Gradient Descent Tricks [PDF]
, 2012 Chapter 1 strongly advocates the stochastic back-propagation method to train neural networks. This is in fact an instance of a more general technique called stochastic gradient descent (SGD). This chapter provides background material, explains why SGD is a good learning algorithm when the training set is large, and provides useful recommendations.
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