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检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

共 13410 条记录，以下是71-80 订阅

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Multi Dimensional Deep Encoding for Categorical Feature Space 24

Multi Dimensional Deep Encoding for Categorical Feature Spac...

引用

13th international conference on computing and Pattern Recognition, ICCPR 2024

作者： Alamuri, Madhavi Surampudi, Bapiraju Negi, Atul School of Computer and Information Sciences University of Hyderabad Telangana State Hyderabad India Cognitive Science Lab International Institute of Information Technology Telangana State Hyderabad India

ISBN: (纸本)9798400717482

Categorical data classification and clustering are essential to many fields, including pattern recognition, data mining, knowledge discovery, and machine learning. It is crucial to understand how to provide categorical input to machine learning algorithms because the majority of them are developed for numerical inputs. Any machine learning algorithm's performance depends not only on the model and hyper parameters but also on the way the data is prepared for and fed into the model. With high cardinality features and categorical predictor variables, machine learning approaches that handle categorical data frequently encounter challenges. In this scenario, we investigate categorical encoding techniques, develop a taxonomy of encoding techniques, and use the machine learning algorithms KNN, MLP, SVM, and ELM and make a comparative study. We suggest a method of hybrid encoding using CNN that gives better performance results. In this paper, we propose the machine learning strategy employing multi-dimensional encodings with CNN, which outperforms other machine learning approaches on majority datasets. It is clear that CNN's performance in learning categorical features has been much enhanced by the use of multi-dimensional encoded input vectors. In our suggested method for supervised and unsupervised encoding, we have used four distinct encodings. These results encourage us to further study the outcomes of different hybrid combinations of encodings for categorical feature classification. © 2024 Copyright held by the owner/author(s).

关键词： Categorical data

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machine learning Optimization Model to Predict Fantasy Basketball Teams 1

Machine Learning Optimization Model to Predict Fantasy Baske...

引用

1st IEEE international conference on computing and data science, ICCDS 2024

作者： Veluru, Vishnuvardhan Xiao, Ting Addagudi, Sohan Kumar, Sehej Mohanraj, Gautham University of North Texas Department of Information Science DentonTX United States University of Minnesota Department of Computer Science MinneapolisMN United States University of North Texas Texas Academy of Math and Science DentonTX United States

ISBN: (纸本)9798350365337

In recent years, statistical analysis has revolution-ized decision-making in the NBA and the growth of Fantasy Basketball platforms like DraftKings and FanDuel. This paper presents a novel approach to draft a fantasy team, utilizing a machine learning optimization model powered by Gurobi solver. Our model efficiently constructs optimal fantasy teams, navigating constraints such as salary caps and player positions. By leveraging player statistics and injury data through metic- ulous data processing and advanced optimization techniques, our methodology maximizes fantasy points while ensuring active player participation. Our study showcases the effectiveness of integrating machine learning optimization models in fantasy basketball team selection, offering valuable insights and practical solutions for enthusiasts seeking a technological competitive edge in fantasy sports. © 2024 IEEE.

关键词： Sports

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FedRL: Federated learning with Non-IID data via Review learning 24

FedRL: Federated Learning with Non-IID Data via Review Learn...

引用

16th international conference on machine learning and computing (ICMLC)

作者： Wang, Jinbo Wang, Ruijin Pei, Xikai Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (纸本)9798400709234

Federated learning epitomizes a sophisticated distributed machine learning methodology, enabling collaborative neural network model training across multiple entities without necessitating the transfer of local data, thereby fortifying data privacy protection. A significant challenge in federated learning lies in the statistical heterogeneity, characterized by non-independent and identically distributed (Non-IID) local data across diverse parties. This heterogeneity can engender inconsistent optimization within individual local models. Although previous research has endeavored to tackle issues stemming from heterogeneous data, our findings indicate that these attempts have not yielded high-performance neural network models. To confront this fundamental challenge, we introduce the FedRL framework in this paper, which facilitates efficient federated learning through review learning. The core principle of FedRL involves leveraging the knowledge representation generated by the global and local model layers to conduct periodic layer-by-layer comparative learning in a reciprocal manner. This strategy rectifies local model training, leading to enhanced outcomes. Our experimental results and subsequent analysis substantiate that FedRL effectively augments model accuracy in image classification tasks, while demonstrating resilience to statistical heterogeneity across all participating entities.

关键词： machine learning Federated learning Heterogeneity data Image classification

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On Optimizing Hyperparameters for Quantum Neural Networks 5

On Optimizing Hyperparameters for Quantum Neural Networks

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2024 international conference on Quantum computing and Engineering

作者： Herbst, Sabrina De Maio, Vincenzo Brandic, Ivona TU Wien Vienna Austria

ISBN: (纸本)9798331541378

The increasing capabilities of machine learning (ML) models go hand in hand with an immense amount of data and computational power required for training. Therefore, training is usually outsourced into HPC facilities, where we have started to experience limits in scaling conventional hardware, as theorized by Moore's law. Despite heavy parallelization and optimization efforts, current state-of-the-art ML models require weeks for training, which is associated with an enormous CO2 footprint. Quantum computing, and specifically Quantum machine learning (QML), can offer significant theoretical speed-ups and enhanced expressive power. However, training QML models requires tuning various hyperparameters, which is a nontrivial task, and suboptimal choices can highly affect the trainability and performance of the models. In this study, we identify main hyperparameters and collect data about the performance of QML models on real-world data. Notably, we build on existing work by also benchmarking different proposed parameter initialization strategies, which should help avoid trainability issues, such as Barren Plateaus. We compare different configurations and provide researchers with performance data and concrete suggestions for hyperparameter selection.

关键词： Quantum computing Quantum machine learning Quantum Neural Network Variational Quantum Algorithm Hyperparameter Optimization

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Smart Commerce: Unleashing machine learning for Optimal Customer Experiences

Smart Commerce: Unleashing Machine Learning for Optimal Cust...

引用

2024 international conference on Electrical, Electronics and computing Technologies, ICEECT 2024

作者： Agarwal, Rachna Agarwal, Shipra Mittal, Varsha Graphic Era Deemed to be University Dept.of Commerce Dehradun India Graphic Era Deemed to be University Dept of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350378092

machine learning is a branch of Artificial Intelligence (AI) and computer technology which address on the usage or application of data and Algorithms to emulate the style that humans learn, gradually improving its accuracy. By understanding this statement humans are not associated with machine learning, once the system is deployed by them. Once the whole system is installed in the software through algorithm or coding, the machine adapts the order and continues accordingly without hindering little things like Sleeping, fatigue like humans and can work for long time and can operate it 24/7, with repetition. But the system must be kept up-to-date. E-commerce or electronic commerce is the trading of goods and services, or transmitting of funds or data, over an electronic network, primarily in internet. In this paper, we will explore the crucial role that machine learning plays in the e-commerce sector, highlighting its impact on various aspects of digital commerce and its importance for organizations in the contemporary business landscape. © 2024 IEEE.

关键词： Adversarial machine learning

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CAUDA: Cloud-assisted Active Unsupervised Domain Adaptation for On-device learning

CAUDA: Cloud-assisted Active Unsupervised Domain Adaptation ...

引用

2024 IEEE international Performance, computing, and Communications conference, IPCCC 2024

作者： Wang, Zefu Zheng, Zhenzhe Wu, Fan Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9798350367942

The recent developments in mobile computing have facilitated the application of on-device machine learning on edge devices. However, complex heterogeneous environmental factors such as network conditions, spatial locations, and device types, can introduce significant unlabeled domain shift, leading to performance degradation in the real-world deployment of on-device learning. In this work, we design CAUDA, a Cloud-assisted Active Unsupervised Domain Adaptation framework for on-device machine learning, to address unlabeled domain shift issue in mobile computing. We propose active filtering method to perform data valuation and select hard-to-adapt samples to label with theoretical guarantees during adaptation. To targetedly process the filtered hard-to-adapt data, we leverage prototype contrastive learning to align them from the perspective of metric learning. The cloud assists on-device learning by annotating filtered unlabeled data and generating corresponding prototypes. Extensive experiments on four tasks for two representative mobile computing applications demonstrate the remarkable advantages of CAUDA with up to 21.38% increase in average inference accuracy compared with other unsupervised domain adaptation and active learning methods. © 2024 IEEE.

关键词： Active learning

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PDE-Net: Pyramid Depth Estimation Network for Light Fields 24

PDE-Net: Pyramid Depth Estimation Network for Light Fields

引用

16th international conference on machine learning and computing (ICMLC)

作者： Wang, Qiong Li, Yan Zhejiang Univ Technol Hangzhou Peoples R China

ISBN: (纸本)9798400709234

Depth information is critical for highly developed robotic systems which need the global perception of their surroundings. A light field camera records with a large amount of data, increasing potentials for the depth estimation improvements. This paper addresses the challenge of performing depth estimation from a light field image: preserving depth discontinuity, which provides robotics with the more precise perception. We design an end-to-end trainable Pyramid Depth Estimation Network PDE-Net, by introducing the multiscale feature merging from stacked images along four directions of light fields to perform the coarse-to-fine estimation. Extensive experiments on both synthetic light field datasets and real-world light field datasets demonstrate the superior performance of the proposed learning-based method compared to the state-of-the-art methods.

关键词： Depth estimation light field imaging deep learning robot vision

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Fossil-Net Lung Cancer Prediction and Classification from CT Images Using Convolution Neural Networks 2

Fossil-Net Lung Cancer Prediction and Classification from CT...

引用

2nd international conference on computing and data Analytics, ICCDA 2024

作者： Deepa, V. Merlin Christo, F. Rajalakshmi Institute of Science and Technology Department of Artificial Intelligence and Data Science TamilNadu Chennai600124 India

ISBN: (纸本)9798331542573

Lung cancer is the leading cause of death worldwide for both men and women. The most well-known disease is the heart disease followed by lung cancer. Early discovery of lung cancer can effectively treat lung cancer at an early stage. The potential for an early diagnosis of lung cancer to increase a patient's chance of survival. goal of this work was to identify lung malignancies in their early stages using supervised learning. The Random forest model outperforms all other supervised machine learning methods for the Prediction of Lung cancer. The Objective of this study is to compare the different types of the machine learning algorithms and provide the better Accuracy. © 2024 IEEE.

关键词： Lung cancer

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Enhanced Crop Yield Prediction using machine learning Techniques 15

Enhanced Crop Yield Prediction using Machine Learning Techni...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Nossam, Sri Chakradhar Katakam, Rishi Anirudh Pulastya, Gopa Venugopalan, Manju Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

In modern agriculture, accurately anticipating crop yield estimation is critical aids of sustainable resource management, efficient decision-making, and food security. This is therefore a process that includes a breakdown of those systems containing data on temperature, rain, yield values, pesticide usage. This process commences with data preprocessing which consist of engineering the features, cleaning, normalization and fusing of discrete data sources. The prediction of agricultural output which is based on the machine learning approach and input interactions is an outcome of this machine learning approach. In the model developed uses several classifiers, that is SVM, Decision Tree, Random Forest, AdaBoost, XGBoost, Naïve Bayes, KNN, MLP, Logistic Regression, Linear Discriminant Analysis, Extra Trees, and Voting Classifiers. The model reported an accuracy of over 99%. This was achieved through the use of Extra Trees technique, which is an ensemble learning method. The reliability of these statistics helps farmers, agricultural policymakers, like the other stakeholders, to make good vital decisions, in turn increasing agricultural production and food security. © 2024 IEEE.

关键词： Clustering Crop Yield Prediction data Integration Exploratory data Analysis Feature Engineering machine learning machine learning Techniques Model Evaluation Model Selection Predictive Model

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Quantifying the Limits of Classical machine learning Models Using Contextuality 5

Quantifying the Limits of Classical Machine Learning Models ...

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2024 international conference on Quantum computing and Engineering

作者： Anschuetz, Eric R. Teo, Mariesa Yang, Willers Sud, James Kang, Christopher Tomesh, Teague Chong, Frederic T. CALTECH Inst Quantum Informat & Matter Pasadena CA 91125 USA Univ Chicago Pritzker Sch Mol Engn Chicago IL 60637 USA Univ Chicago Dept Comp Sci Chicago IL 60637 USA Infleation Chicago IL USA

ISBN: (纸本)9798331541378

Classical machine learning models struggle with learning and prediction tasks on data sets exhibiting long-range correlations. To quantify this observation we introduce a new quantity we call strong k-contextuality, develop efficient algorithms to estimate the strong k-contextuality of an empirical data set, and prove that the presence of strong k-contextuality lower bounds the classical resources required to model the associated distribution. We also show that this correlation measure does not induce a similar resource lower bound for quantum generative models, and thus propose strong k-contextuality as an empirical measure for evaluating whether a given machine learning task may be better suited for quantum models than classical models.

关键词： Quantum machine learning Quantum Advantage Contextuality

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