检索结果-内蒙古大学图书馆

Hypothesis testing with e-values

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Ramdas, Aaditya Wang, Ruodu Department of Statistics and Data Science Machine Learning Department Carnegie Mellon University United States Department of Statistics and Actuarial Science University of Waterloo Canada

This book is written to offer a humble, but unified, treatment of e-values inhypothesis testing. The book is organized into three parts: FundamentalConcepts, Core Ideas, and Advanced Topics. The first part includes threechapters that introduce the basic concepts. The second part includes fivechapters of core ideas such as universal inference, log-optimality,e-processes, operations on e-values, and e-values in multiple testing. Thethird part contains five chapters of advanced topics. We hope that, by puttingthe materials together in this book, the concept of e-values becomes moreaccessible for educational, research, and practical use. Copyright © 2024, The Authors. All rights reserved.

关键词：

Estimating relative diffusion from 3D micro-CT images using CNNs

学校读者我要写书评

暂无评论

Artificial Intelligence in Geosciences 2023年第1期4卷 199-208页

作者： Stephan Gättner Florian Frank Fabian Woller Andreas Meier Nadja Ray Friedrich-Alexander-Universitat Erlangen-Nürnberg Department MathematikCauerstraβe 11Erlangen91058Germany Math2 Market GmbH Richard-Wagner-Straβe 1Kaiserslautern67655Germany Mathematical Institute for Machine Learning and Data Science Goldknopfgasse 7Ingolstadt49085Germany

In recent years,convolutional neural networks(CNNs)have demonstrated their effectiveness in predicting bulk parameters,such as effective diffusion,directly from pore-space *** offer significant computational advantages over traditional methods,making them particularly ***,the current literature primarily focuses on fully saturated porous media,while the partially saturated case is also of high interest for various *** saturated conditions present more complex geometries for diffusive transport,making the prediction task more *** CNNs tend to lose robustness and accuracy with lower saturation *** this paper,we overcome this limitation by introducing a CNN,which conveniently fuses diffusion prediction and a well-established morphological model that describes phase distributions in partially saturated porous *** demonstrate the ability of our CNN to perform accurate predictions of relative diffusion directly from full pore-space ***,we compare our predictions with well-established relations such as the one by Millington–Quirk.

关键词： Digital rock Neural networks Deep learning Relative diffusion Partial saturation

Method for the Automatic Modulation Classification for the Sums of Phase- Shift-Keying Signals Based on Polynomial Features

学校读者我要写书评

暂无评论

Method for the Automatic Modulation Classification for the S...

IEEE International Conference on Electronics and Nanotechnology (ELNANO)

作者： Vasyl Semenov Yevheniya V. Semenova Laboratory of Data Science and Machine Learning Kyiv Academic University American University Kyiv Delta SPE LLC Kyiv Ukraine Department of Numerical Mathematics Institute of Mathematics of NANU Laboratory of Data Science and Machine Learning Kyiv Academic University Kyiv Ukraine

ISBN: (数字)9798350368178

ISBN: (纸本)9798350368185

This paper considers the task of automatic modulation classification for the sums of phase-shift keying (PSK) signals which occupy the same frequency bandwidth and have the same modulation. This problem is an important step which precedes the subsequent processing of such signals. For the solution of this classification problem we implement the method based on two machine learning tools: logistic regression or two-layer feedforward neural network. It allows to determine the class to which the sum of two PSK signals belongs. As the features used for the input of these classifications tools, we use second-order polynomial features calculated based on higher order cumulants of the received signal. The results of numerical experiments using different channel parameters and noise levels show the advantage of implementing second-order features over the raw features both in the case of logistic regression and two-layer feedforward neural network.

关键词： Logistic regression Frequency modulation databases Simulation machine learning Polynomials Feedforward neural networks Binary phase shift keying Nanotechnology Noise level

Investor Risk Profile Determination Model 16

学校读者我要写书评

暂无评论

Investor Risk Profile Determination Model

16th International Conference Management of Large-Scale System Development, MLSD 2023

作者： Gorelik, Victor Zolotova, Tatiana Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Department of Simulation Systems and Operations Research Moscow Russia Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia

ISBN: (纸本)9798350337907

An assessment of the investor's risk profile is proposed as a risk coefficient in a model with a linear convolution of expected return and variance. The value of the risk coefficient is found from solving the optimization problem of the model with a limitation on profitability. The proposed method is implemented in the form of a software package using the example of the stock market. © 2023 IEEE.

关键词： Convolution

Optimization and Benchmarking of Convolutional Networks with Quantization and OpenVINO in Baggage Image Recognition 8

学校读者我要写书评

暂无评论

Optimization and Benchmarking of Convolutional Networks with...

8th International Conference on Information Technology and Nanotechnology, ITNT 2022

作者： Andriyanov, Nikita Papakostas, George Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia International Hellenic University Department of Computer Science Thessaloniki Greece

ISBN: (数字)9781665485715

ISBN: (纸本)9781665485715

The paper is devoted to the study of the neural networks inference acceleration using the weights quantization and Intel OpenVINO Toolkit. At the same time, the study considers block architecture convolutional networks trained from scratch. In addition, it is shown that transfer learning makes it possible to obtain higher model accuracy. And their implementation on OpenVINO can significantly increase the processing performance. It is also shown that the use of OpenVINO provides significant acceleration without loss of performance for such networks, while quantization leads to significant loss of quality. © 2022 IEEE.

关键词： Computer vision

Positive Semidefinite Matrix Supermartingales

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Wang, Hongjian Ramdas, Aaditya Department of Statistics and Data Science Carnegie Mellon University United States Machine Learning Department Carnegie Mellon University United States

We explore the asymptotic convergence and nonasymptotic maximal inequalities of supermartingales and backward submartingales in the space of positive semidefinite matrices. These are natural matrix analogs of scalar nonnegative supermartingales and backward nonnegative submartingales, whose convergence and maximal inequalities are the theoretical foundations for a wide and ever-growing body of results in statistics, econometrics, and theoretical computer science. Our results lead to new concentration inequalities for either martingale dependent or exchangeable random symmetric matrices under a variety of tail conditions, encompassing now-standard Chernoff bounds to self-normalized heavy-tailed settings. Further, these inequalities are usually expressed in the Loewner order, are sometimes valid simultaneously for all sample sizes or at an arbitrary data-dependent stopping time, and can often be tightened via an external randomization *** Codes 60B20, 60G48, 62L10 Copyright © 2024, The Authors. All rights reserved.

关键词： Matrix algebra

Sharp Matrix Empirical Bernstein Inequalities

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Wang, Hongjian Ramdas, Aaditya Department of Statistics and Data Science Carnegie Mellon University United States Machine Learning Department Carnegie Mellon University United States

We present two sharp empirical Bernstein inequalities for symmetric random matrices with bounded eigenvalues. By sharp, we mean that both inequalities adapt to the unknown variance in a tight manner: the deviation captured by the first-order 1/√n term asymptotically matches the matrix Bernstein inequality exactly, including constants, the latter requiring knowledge of the variance. Our first inequality holds for the sample mean of independent matrices, and our second inequality holds for a mean estimator under martingale dependence at stopping times. Copyright © 2024, The Authors. All rights reserved.

关键词： Matrix algebra

Customer Segmentation Using Clustering Analysis

学校读者我要写书评

暂无评论

Customer Segmentation Using Clustering Analysis

2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Jiet, Moses Makuei Kamble, Aahash Puri, Chetan Yesankar, Prajyot Verma, Prateek Rewatkar, Rajendra Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Data Science Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Machine Learning Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Biomedical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798350382693

This research focuses on the crucial role of the clustering technique in data mining, specifically in market forecasting and planning. The study presents a comprehensive report on utilizing the k-means clustering technique and data visualization tools to develop a real- time system for a supermarket, predicting sales across various annual cycles. An open-source dataset of a mall with n = 200 customers divided into k = 3 groups, demonstrated high accuracy in predicting day-to-day sales statistics when compared with actual results. The research highlights the effectiveness of the clustering technique in real-time sales prediction for supermarkets. © 2024 IEEE.

关键词： Sales

Testing by Betting while Borrowing and Bargaining

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Wang, Hongjian Ramdas, Aaditya Department of Statistics and Data Science Carnegie Mellon University United States Machine Learning Department Carnegie Mellon University United States

Testing by betting has been a cornerstone of the game-theoretic statistics literature. In this framework, a betting score (or more generally an e-process), as opposed to a traditional p-value, is used to quantify the evidence against a null hypothesis: the higher the betting score, the more money one has made betting against the null, and thus the larger the evidence that the null is false. A key ingredient assumed throughout past works is that one cannot bet more money than they currently have. In this paper, we ask what happens if the bettor is allowed to borrow money after going bankrupt, allowing further financial flexibility in this game of hypothesis testing. We propose various definitions of (adjusted) evidence relative to the wealth borrowed, indebted, and accumulated. We also ask what happens if the bettor can "bargain", in order to obtain odds bettor than specified by the null hypothesis. The adjustment of wealth in order to serve as evidence appeals to the characterization of arbitrage, interest rates, and numéraire-adjusted pricing in this setting. Copyright © 2024, The Authors. All rights reserved.

关键词： Game theory