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检索条件"主题词=expectation maximization algorithm"

共 651 条记录，以下是411-420 订阅

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Automatic Stock Profit maximization via Deep Reinforcement Learning and Correlation Analysis 15

Automatic Stock Profit Maximization via Deep Reinforcement L...

引用

15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Park, Soohyun Joo, Minjoo Sookmyung Women's University Division of Computer Science Seoul Korea Republic of

ISBN: (纸本)9798350364637

In this paper, we propose a novel Deep Deterministic Policy Gradient (DDPG)-based stock profit maximization approach with enhanced predictability and strategic decision-making by integrating correlation analysis between stocks. The proposed algorithm is designed to consider user budgets as well as evaluated to utilize real-world stock market data. Lastly, the performance of the proposed method is evaluated and verified that our proposed method outperforms the others. © 2024 IEEE.

关键词： expectation maximization algorithm

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Label Propagation algorithm Based on 2-Hop Neighbor and Weighted Path for Community Detection 7

Label Propagation Algorithm Based on 2-Hop Neighbor and Weig...

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7th International Conference on Data Science and Information Technology, DSIT 2024

作者： Feng, Boya Shanghai Jiao Tong University Department of Electronic Engineering Shanghai China

ISBN: (纸本)9798350384093

Community detection is a research focus in the field of complex networks and has wide applications in various domains. However, traditional label propagation algorithms and modularity maximization algorithms often fail to achieve optimal results in many community detection tasks. By observing the characteristics of real-world social networks, this paper proposes a label propagation algorithm based on 2-hop neighbor and weighted path (LPANW) for community detection. The proposed algorithm is compared with three other algorithms on both synthetic networks and real-world datasets, demonstrating better NMI index and robustness. © 2024 IEEE.

关键词： expectation maximization algorithm

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Choosing Behind the Veil: Tight Bounds for Identity-Blind Online algorithms 24

Choosing Behind the Veil: Tight Bounds for Identity-Blind On...

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25th Conference on Economics and Computation, EC 2024

作者： Ezra, Tomer Feldman, Michal Tang, Zhihao Gavin Harvard University Cambridge United States Tel Aviv University Microsoft ILDC Tel Aviv Israel ITCS Key Laboratory of Interdisciplinary Research of Computation and Economics Shanghai University of Finance and Economics Shanghai China

ISBN: (纸本)9798400707049

In Bayesian online settings, every element is associated with a value drawn from a known underlying distribution. This distribution, representing the population from which the element is drawn, is referred to as the element’s identity. The elements arrive sequentially, with their values being revealed in an online manner. Most previous work has assumed that, upon the arrival of a new element, the online algorithm observes its value and its identity. However, practical scenarios frequently require algorithms to make decisions based solely on the element’s value, disregarding its identity. This necessity emerges either from the algorithm’s lack of knowledge about the element’s identity or in the pursuit of fairness, aiming for bias-free decisions across varying identities. We call such algorithms identity-blind algorithms, and propose the identity-blindness gap as a metric to evaluate the performance loss in online algorithms caused by identity-blindness. This gap is defined as the maximum ratio between the expected performance of an identity-blind online algorithm and an optimal online algorithm that knows the arrival order, thus also the identities. We study the identity-blindness gap in the paradigmatic prophet inequality problem, under the two common objectives of maximizing the expected value, and maximizing the probability to obtain the highest value. We provide tight bounds with respect to both objectives. For the max-expectation objective, the celebrated prophet inequality establishes a single-threshold (thus identity-blind) algorithm that gives at least 1/2 of the offline optimum, thus also an identity-blindness gap of at least 1/2. We show that this bound is tight with respect to the identity-blindness gap, even with respect to randomized algorithms. For the max-probability objective, we provide a deterministic single-threshold (thus identity-blind) algorithm that gives an identity-blindness gap of ∼ 0.562 (assuming the absence of large point masses). Moreov

关键词： expectation maximization algorithm

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Clustering High-Dimensional Spaces Using a Modified EM algorithm with Fractional Order Assignment 4

Clustering High-Dimensional Spaces Using a Modified EM Algor...

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4th International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Saxena, Shivani Rajyaguru, Prince Saxena, Shruti Patel, Nilesh Rizvi, Ahsan Institute of Advanced Research Gujarat Gandhinagar India Spec India Gujarat Ahmedabad India School of Cyber Security and Digital Forensics National Forensics Sciences University Gujarat Gandhinagar India Umeå Plant Science Centre Swedish University of Agricultural Sciences Umeå Sweden

ISBN: (纸本)9798331506490

Clustering high-dimensional data presents significant challenges due to the curse of dimensionality, which complicates the detection of meaningful patterns and clusters. Traditional methods struggle to manage the increasing complexity, often becoming sensitive to noise, inefficient in capturing intricate relationships, and unable to handle uncertainties effectively. To address these issues, this study introduces EM-FASL, a novel clustering algorithm designed to improve accuracy in high-dimensional spaces by accounting for complex relationships and uncertainties. EM-FASL is an adaptation of the expectation-maximization (EM) algorithm, enhanced through fractional order assignment and logistic regression (LR). The method begins by initializing the parameters for a Gaussian Mixture Model (GMM) and an LR model. In the expectation (E) step, it fits the GMM, computes fractional assignments, and evaluates fractional entropy to measure the uncertainty in assignments. In the maximization (M) step, the GMM parameters are updated using the fractional assignments, while LR coefficients adjust the mean vectors of the GMM. The algorithm iterates between these steps until fractional entropy converges. The algorithm is evaluated on a range of high-dimensional datasets from the UCI repository, demonstrating its robustness across diverse data structures. Experimental results demonstrate that EM-FASL significantly outperforms existing clustering methods, offering superior accuracy in high-dimensional data by effectively capturing intricate data patterns. This innovative approach provides a robust solution to the challenges of high-dimensional clustering and opens up new avenues for future research. © 2024 IEEE.

关键词： expectation maximization algorithm

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Turbo Inverse-Free Successive Linear Approximation VBI for Joint Grid Parameters and Channel Estimation in OTFS Systems 100

Turbo Inverse-Free Successive Linear Approximation VBI for J...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Qiu, Sijia Lei, Ming Zhao, Ming-Min Cai, Yunlong Zhao, Minjian Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Hangzhou310027 China

ISBN: (纸本)9798331517786

For reliable communication in high mobility scenarios, we need to estimate the channel in orthogonal time frequency space (OTFS) systems, which can be considered as a sparse signal recovery problem with an uncertain sensing matrix and solved by compressed sensing (CS) algorithms. However, conventional expectation maximization (EM)-based CS algorithms only output the point estimation of grid parameters to approximate the sensing matrix, which leads to an unavoidable approximation error. To address this problem, we present a turbo inverse-free successive linear approximation variational Bayesian inference (Turbo-IFSLA-VBI) algorithm, which provides the Bayesian estimation of both channel and grid parameters, thus the approximation error can be eliminated by iteratively approximating the sensing matrix with updated grid parameters. Besides, the proposed method employs a majorization-minimization (MM) framework to simplify the matrix inverse operations, achieving a lower computational complexity. Finally, simulation results are presented to verify the superiority of the proposed scheme over the state-of-the-art schemes. © 2024 IEEE.

关键词： expectation maximization algorithm

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Low Power Consumption Optimization Control of Air Conditioning for UAV Nest on Tower Based on EM algorithm 8

Low Power Consumption Optimization Control of Air Conditioni...

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8th International Workshop on Control Engineering and Advanced algorithms, IWCEAA 2024

作者： Wang, Miao Liu, Junnan Wu, Xiaojie Wu, Wenbin Ye, Jianfeng State Grid Electric Power Space Technology Co. Ltd. Beijing102200 China State Grid Fujian Electric Power Research Institute Fujian Fuzhou350007 China

ISBN: (纸本)9798331528881

Aiming at the problem of high energy consumption of air conditioning system of UAV nest on tower, a low power optimization control method based on expectation maximization (EM) algorithm is proposed. Based on the analysis of the factors affecting the temperature of the UA V nest on the tower, the air conditioning control model of the UA V nest on the tower is constructed. The operating mode of the air conditioning is dynamically adjusted using the EM algorithm, which can significantly reduce the energy consumption while ensuring the stability of the temperature inside the nest, effectively improve the energy efficiency ratio of the air conditioning system, and provide a new solution for the energy-saving operation of the UAV nest. © 2024 IEEE.

关键词： expectation maximization algorithm

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Decentralized Message-Passing for Semi-Blind Channel Estimation in Cell-Free Systems Based on Bethe Free Energy Optimization 58

Decentralized Message-Passing for Semi-Blind Channel Estimat...

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58th Asilomar Conference on Signals, Systems and Computers, ACSSC 2024

作者： Zhao, Zilu Slock, Dirk Eurecom Communication Systems Department France

ISBN: (纸本)9798350354058

In this work, we investigate uplink communication in Semi-Blind Cell-Free (CF) Massive Multiple-Input Multiple-Output (MaMIMO) systems. One of the major challenges in CF MaMIMO systems is pilot contamination, where multiple user terminals (UTs) may use the same pilot sequence due to an imbalance between the number of UTs and the length of the pilot sequence. Semi-blind approaches have been proposed to address this issue, where access points (APs) jointly estimate both the channel and user data. This joint estimation leads to a bilinear problem. Channel estimation in bilinear systems with Gaussian input has been studied in prior work, with expectation propagation (EP)-based algorithms, such as variable-level (VL)-EP and hybrid expectation maximization (EM)-EP, being proposed. However, in this paper, the user data follows a categorical distribution. To develop a tractable algorithm that leverages the finite alphabet of the user data, we investigate the Bethe free energy (BFE) of the bilinear system and propose a message-passing algorithm by minimizing the BFE. The resulting algorithm combines variational Bayes (VB), belief propagation (BP), and EP. © 2024 IEEE.

关键词： expectation maximization algorithm

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Mixed State Variational Quantum Eigensolver for the Estimation of expectation Values at Finite Temperature 40

Mixed State Variational Quantum Eigensolver for the Estimati...

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40th International Symposium on Lattice Field Theory, LATTICE 2023

作者： Clemente, Giuseppe Deutsches Elektronen-Synchrotron DESY Platanenallee 6 Zeuthen15738 Germany Dipartimento di Fisica dell'Università di Pisa INFN - Sezione di Pisa Largo Pontecorvo 3 PisaI-56127 Italy

We introduce a novel hybrid quantum-classical algorithm for the near-term computation of expectation values in quantum systems at finite temperatures. This is based on two stages: on the first one, a mixed state approximating a fiducial truncated density matrix is prepared through Variational Quantum Eigensolving (VQE) techniques;this is then followed by a reweighting stage where the expectation values for observables of interest are computed. These two stages can then be iterated again with different hyperparameters to achieve arbitrary accuracy. Resource and time scalability of the algorithm is discussed with a near-term perspective. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： expectation maximization algorithm

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Using Mixed Exponentials for Unsupervised Discretization 9th

Using Mixed Exponentials for Unsupervised Discretization

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9th Future Technologies Conference, FTC 2024

作者： Dias, Dulan S. Marshall, Adele H. Novakovic, Aleksandar School of Mathematics and Physics Queen’s University Belfast University Road BelfastBT7 1NN United Kingdom Faculty of Business and IT Ontario Tech University 2000 Simcoe Street North OshawaONL1G 0C5 Canada

ISBN: (纸本)9783031731242

In this study, discretization, fundamental to numerous data analysis and modeling activities, is reconceptualized through an innovative, unsupervised approach. This method enhances the interpretability and simplifies the management of continuous variables, especially those exhibiting right-skewed distributions. By initially fitting a variable to a mixture of exponential distributions using the expectation-maximization algorithm, and determining discretization cutpoints via intersections of the component distributions, our proposed approach outperforms traditional methods like Equal width and Equal Frequency. The method significantly enhances classification accuracy across various machine learning models, including Random Forest, Neural Network, and Gradient Boosting Machine, with improvements ranging from approximately 5% to 25%. Furthermore, it increases mutual information (between 1.5 to 600 times) and consistently maintains a stability measure of one. These results were observed across the three datasets evaluated. The paper highlights the potential of this proposed discretization method, discusses its promising applications, and indicates future research directions, emphasizing its superior performance especially for right-skewed distributions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

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A General Online algorithm for Optimizing Complex Performance Metrics 41

A General Online Algorithm for Optimizing Complex Performanc...

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41st International Conference on Machine Learning, ICML 2024

作者： Kotlowski, Wojciech Wydmuch, Marek Schultheis, Erik Babbar, Rohit Dembczyński, Krzysztof Poznan University of Technology Poznan Poland Aalto University Helsinki Finland University of Bath Bath United Kingdom Yahoo Research New York United States

We consider sequential maximization of performance metrics that are general functions of a confusion matrix of a classifier (such as precision, F-measure, or G-mean). Such metrics are, in general, non-decomposable over individual instances, making their optimization very challenging. While they have been extensively studied under different frameworks in the batch setting, their analysis in the online learning regime is very limited, with only a few distinguished exceptions. In this paper, we introduce and analyze a general online algorithm that can be used in a straightforward way with a variety of complex performance metrics in binary, multi-class, and multi-label classification problems. The algorithm's update and prediction rules are appealingly simple and computationally efficient without the need to store any past data. We show the algorithm attains O(lnnn ) regret for concave and smooth metrics and verify the efficiency of the proposed algorithm in empirical studies. Copyright 2024 by the author(s)

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