Relevant bibliographies by topics / Machine Learning Feature Stores

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Machine Learning Feature Stores'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Machine Learning Feature Stores.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Machine Learning Feature Stores"

1

Researcher. "MACHINE LEARNING FEATURE STORES: A COMPREHENSIVE OVERVIEW." International Journal of Computer Engineering and Technology (IJCET) 15, no. 5 (2024): 82–91. https://doi.org/10.5281/zenodo.13711230.

Full text
Abstract:
This article presents a comprehensive examination of Machine Learning (ML) Feature Stores, their role in modern ML infrastructures, and their impact on the efficiency and scalability of ML operations. We explore the key roles of Feature Stores, including centralization of feature management, ensuring consistency between training and serving environments, promoting feature reusability, enhancing governance, and improving overall efficiency in ML workflows. A detailed reference architecture is proposed, outlining essential components such as data ingestion, feature engineering, storage, serving,
APA, Harvard, Vancouver, ISO, and other styles
2

Liu, Rui, Kwanghyun Park, Fotis Psallidas, et al. "Optimizing Data Pipelines for Machine Learning in Feature Stores." Proceedings of the VLDB Endowment 16, no. 13 (2023): 4230–39. http://dx.doi.org/10.14778/3625054.3625060.

Full text
Abstract:
Data pipelines (i.e., converting raw data to features) are critical for machine learning (ML) models, yet their development and management is time-consuming. Feature stores have recently emerged as a new "DBMS-for-ML" with the premise of enabling data scientists and engineers to define and manage their data pipelines. While current feature stores fulfill their promise from a functionality perspective, they are resource-hungry---with ample opportunities for implementing database-style optimizations to enhance their performance. In this paper, we propose a novel set of optimizations specifically
APA, Harvard, Vancouver, ISO, and other styles
3

Gupta, Robin. "Real-Time Data Pipelines for Feature Stores in Gaming." Journal for Research in Applied Sciences and Biotechnology 2, no. 5 (2023): 253–65. http://dx.doi.org/10.55544/jrasb.2.5.35.

Full text
Abstract:
Machine learning models are used in content creation and generate real-time observations in gaming with a positive effect on both performance and production processes. However, the management and deployment of these features and metrics for the purposes of these benefits are critical. Looking at feature and metric stores data structures that are used for storing and retrieving feature and metric data for machine learning models. Feature stores are responsible for featuring storage and delivery for model training and features needed for model’s inferencing, whereas metric stores contain metrics
APA, Harvard, Vancouver, ISO, and other styles
4

Majeed, Abdul, and Seong Oun Hwang. "Feature Stores: A Key Enabler for Feature Reusability and Availability Across Machine Learning Pipelines." Computer 57, no. 1 (2024): 69–74. http://dx.doi.org/10.1109/mc.2023.3308868.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Orr, Laurel, Atindriyo Sanyal, Xiao Ling, Karan Goel, and Megan Leszczynski. "Managing ML pipelines." Proceedings of the VLDB Endowment 14, no. 12 (2021): 3178–81. http://dx.doi.org/10.14778/3476311.3476402.

Full text
Abstract:
The industrial machine learning pipeline requires iterating on model features, training and deploying models, and monitoring deployed models at scale. Feature stores were developed to manage and standardize the engineer's workflow in this end-to-end pipeline, focusing on traditional tabular feature data. In recent years, however, model development has shifted towards using self-supervised pretrained embeddings as model features. Managing these embeddings and the downstream systems that use them introduces new challenges with respect to managing embedding training data, measuring embedding qual
APA, Harvard, Vancouver, ISO, and other styles
6

Yi, Siming. "Walmart Sales Prediction Based on Machine Learning." Highlights in Science, Engineering and Technology 47 (May 11, 2023): 87–94. http://dx.doi.org/10.54097/hset.v47i.8170.

Full text
Abstract:
Accurate sales forecasting can improve a company's profitability while minimizing expenditures. The use of machine learning algorithms to predict product sales has become a hot topic for researchers and companies over the past few years. This report features the machine learning sales prediction model that combines the ML algorithm and meticulous feature engineering processing to predict Walmart sales. The following regressions are analyzed in this paper: linear regression, random forest regression, and XGBoost regression. The regression analysis has been tested for the same time period every
APA, Harvard, Vancouver, ISO, and other styles
7

Gujar, Prof Anil D., Nikita B. Sawant, Tejas L. Hake, Aadesh A. Shete, and Shreekar M. Deshmukh. "Face Recognition Open CV Based ATM Security System." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 1114–19. http://dx.doi.org/10.22214/ijraset.2022.42230.

Full text
Abstract:
Abstract: The real-time face detection and recognition has been made possible by using the method of Viola jones, Analysis work. The software first taking images of all persons and stores the information into database. Proposed work deals with automated system to detect person. The methodology comprised of three phases, first face Detection from image, second get all detail of face for the purpose of feature extraction. The most useful and unique features of the camera image are extracted in the feature extraction phase. Find out all facial details are visible. This feature vector forms an eff
APA, Harvard, Vancouver, ISO, and other styles
8

Sujatha, CN. "Coal Production Analysis using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 919–26. http://dx.doi.org/10.22214/ijraset.2021.35130.

Full text
Abstract:
Coal will keep on giving a significant segment of energy prerequisites in the US for at any rate the following quite a few years. It is basic that exact data portraying the sum, area, and nature of the coal assets and stores be accessible to satisfy energy needs. It is likewise significant that the US separate its coal assets productively, securely, and in a naturally mindful way. A restored center around government support for coal-related examination, facilitated across offices and with the dynamic cooperation of the states and modern area, is a basic component for every one of these necessi
APA, Harvard, Vancouver, ISO, and other styles
9

Palma, Catarina, Artur Ferreira, and Mário Figueiredo. "Explainable Machine Learning for Malware Detection on Android Applications." Information 15, no. 1 (2024): 25. http://dx.doi.org/10.3390/info15010025.

Full text
Abstract:
The presence of malicious software (malware), for example, in Android applications (apps), has harmful or irreparable consequences to the user and/or the device. Despite the protections app stores provide to avoid malware, it keeps growing in sophistication and diffusion. In this paper, we explore the use of machine learning (ML) techniques to detect malware in Android apps. The focus is on the study of different data pre-processing, dimensionality reduction, and classification techniques, assessing the generalization ability of the learned models using public domain datasets and specifically
APA, Harvard, Vancouver, ISO, and other styles
10

Rao, Faizan Ali, Muneer Amgad, Almaghthawi Ahmed, Alghamdi Amal, Mohamed Fati Suliman, and Abdulwasea Abdullah Ghaleb Ebrahim. "BMSP-ML: big mart sales prediction using different machine learning techniques." International Journal of Artificial Intelligence (IJ-AI) 12, no. 2 (2023): 874–83. https://doi.org/10.11591/ijai.v12.i2.pp874-883.

Full text
Abstract:
Variations in sales over time is the main issue faced by many retailers. To overcome this problem, we attempt to predict the sales by comparing the previous sales data of different stores. Firstly, the primary task is to recognize the pattern of the factors that help to predict sales. This study helps us understand the data and predict sales using many machines learning models. This process gets the data and beautifies the data by imputing the missing values and feature engineering. While solving this problem, predicting the monthly sales value is significant in the study. In addition, an esse
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Machine Learning Feature Stores"

1

Tan, Feng. "Improving Feature Selection Techniques for Machine Learning." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/cs_diss/27.

Full text
Abstract:
As a commonly used technique in data preprocessing for machine learning, feature selection identifies important features and removes irrelevant, redundant or noise features to reduce the dimensionality of feature space. It improves efficiency, accuracy and comprehensibility of the models built by learning algorithms. Feature selection techniques have been widely employed in a variety of applications, such as genomic analysis, information retrieval, and text categorization. Researchers have introduced many feature selection algorithms with different selection criteria. However, it has been disc
APA, Harvard, Vancouver, ISO, and other styles
2

Lorentzon, Matilda. "Feature Extraction for Image Selection Using Machine Learning." Thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-142095.

Full text
Abstract:
During flights with manned or unmanned aircraft, continuous recording can result in avery high number of images to analyze and evaluate. To simplify image analysis and tominimize data link usage, appropriate images should be suggested for transfer and furtheranalysis. This thesis investigates features used for selection of images worthy of furtheranalysis using machine learning. The selection is done based on the criteria of havinggood quality, salient content and being unique compared to the other selected images.The investigation is approached by implementing two binary classifications, one
APA, Harvard, Vancouver, ISO, and other styles
3

Keelan, Oliver, and Henrik Mårtensson. "Feature Engineering and Machine Learning for Driver Sleepiness Detection." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-142001.

Full text
Abstract:
Falling asleep while operating a moving vehicle is a contributing factor to the statistics of road related accidents. It has been estimated that 20% of all accidents where a vehicle has been involved are due to sleepiness behind the wheel. To prevent accidents and to save lives are of uttermost importance. In this thesis, given the world’s largest dataset of driver participants, two methods of evaluating driver sleepiness have been evaluated. The first method was based on the creation of epochs from lane departures and KSS, whilst the second method was based solely on the creation of epochs ba
APA, Harvard, Vancouver, ISO, and other styles
4

Battle, Rick. "Machine learning feature selection for tuning memory page swapping." Thesis, Monterey, California: Naval Postgraduate School, 2013. http://hdl.handle.net/10945/37585.

Full text
Abstract:
Approved for public release; distribution is unlimited<br>This thesis is an exploration of the virtual memory subsystem in the modern Linux kernel. It applies machine learning to find areas where better page-out decisions can be made. Two areas of possible improvement are identified and analyzed. The first area explored arises because pages in a computation appear repeatedly in a sequence. This is an example of temporal locality. In this instance, we can predict pages that will not be recalled again from the backing store with a precision and recall of 0.82 and 0.81, respectively, with a bas
APA, Harvard, Vancouver, ISO, and other styles
5

Wang, Alex Christopher. "Feature Factory : a collaborative, crowd-sourced machine learning system." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100859.

Full text
Abstract:
Thesis: M. Eng. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (page 71).<br>In this thesis, I designed, implemented, and tested a machine learning learning system designed to crowd-source feature discovery called Feature Factory. Feature Factory provides a
APA, Harvard, Vancouver, ISO, and other styles
6

Davis, Jonathan J. "Machine learning and feature engineering for computer network security." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/106914/1/Jonathan_Davis_Thesis.pdf.

Full text
Abstract:
This thesis studies the application of machine learning to the field of Cyber security. Machine learning algorithms promise to enhance Cyber security by identifying malicious activity based only on provided examples. However, a major difficulty is the unsuitability of raw Cyber security data as input. In an attempt to address this problem, this thesis presents a framework for automatically constructing relevant features suitable for machine learning directly from network traffic. We then test the effectiveness of the framework by applying it to three Cyber security problems: HTTP tunnel detect
APA, Harvard, Vancouver, ISO, and other styles
7

Russeil, Etienne. "Feature engineering and machine learning for 21st century astronomy." Electronic Thesis or Diss., Université Clermont Auvergne (2021-...), 2024. http://www.theses.fr/2024UCFA0102.

Full text
Abstract:
Les phénomènes transitoires astronomiques comptent parmi les événements les plus énergétiques de l'univers. Afin de percer leurs secrets, des télescopes de plus en plus performants ont été construits pour effectuer des relevés du ciel à grande échelle. Le futur observatoire Vera-C.-Rubin représente l'état de l'art d'une nouvelle génération de tels relevés. Il devrait détecter environ 10 millions de potentiels phénomènes transitoires chaque nuit, et produire une courbe de lumière pour chacun d'entre eux. Compte tenu de ce volume de données sans précédent, l'utilisation de méthodes d'apprentissa
APA, Harvard, Vancouver, ISO, and other styles
8

Lan, Xiangyuan. "Multi-cue visual tracking: feature learning and fusion." HKBU Institutional Repository, 2016. https://repository.hkbu.edu.hk/etd_oa/319.

Full text
Abstract:
As an important and active research topic in computer vision community, visual tracking is a key component in many applications ranging from video surveillance and robotics to human computer. In this thesis, we propose new appearance models based on multiple visual cues and address several research issues in feature learning and fusion for visual tracking. Feature extraction and feature fusion are two key modules to construct the appearance model for the tracked target with multiple visual cues. Feature extraction aims to extract informative features for visual representation of the tracked ta
APA, Harvard, Vancouver, ISO, and other styles
9

Drangel, Andreas. "Feature extraction from images with augmented feature inputs." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-219073.

Full text
Abstract:
Machine learning models for visual recognition tasks such as image recognition is a common research area as of lately. However, not much research has been made when multiple features are to be extracted from the same input. This thesis researches if and how knowledge about one feature influences model performance of a model classifying another feature, as well as how the similarity and generality of the feature data distributions influences model performance. Incorporating augmentation inputs in the form of extra feature information in image models was found to yield different results dependin
APA, Harvard, Vancouver, ISO, and other styles
10

Jonsson, Erik. "Channel-Coded Feature Maps for Computer Vision and Machine Learning." Doctoral thesis, Linköping : Department of Electrical Engineering, Linköpings universitet, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-11040.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Machine Learning Feature Stores"

1

Erik, Jonsson. Channel-coded feature maps for computer vision and machine learning. Department of Electrical Engineering, Linko pings universitet, 2008.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

Hinders, Mark K. Intelligent Feature Selection for Machine Learning Using the Dynamic Wavelet Fingerprint. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49395-0.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Krawiec, Krzysztof. Evolutionary feature programming: Cooperative learning for knowledge discovery and computer vision. Wydawn. Politechniki Poznańskiej, 2004.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Jensen, Richard. Computational intelligence and feature selection: Rough and fuzzy approaches. Wiley, 2008.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

Polyakova, Anna, Tat'yana Sergeeva, and Irina Kitaeva. The continuous formation of the stochastic culture of schoolchildren in the context of the digital transformation of general education. INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1876368.

Full text
Abstract:
The material presented in the monograph shows the possibilities of continuous teaching of mathematics at school, namely, the significant potential of modern information and communication technologies, with the help of which it is possible to form elements of stochastic culture among students. Continuity in learning is considered from two positions: procedural and educational-cognitive. In addition, a distinctive feature of the book is the presentation of the digital transformation of general education as a way to overcome the "new digital divide". Methodological features of promising digital t
APA, Harvard, Vancouver, ISO, and other styles
6

J, Jayanth Kumar M. Feature Store for Machine Learning: Curate, Discover, Share and Serve ML Features at Scale. Packt Publishing, Limited, 2022.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
7

Feature Store for Machine Learning: Curate, Discover, Share and Serve ML Features at Scale. de Gruyter GmbH, Walter, 2022.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

Feature Selection in Machine Learning with Python. Lulu Press, Inc., 2022.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
9

RAO, Landa, and Karakavalasa DURGA AKHIL. Feature Projection in Machine Learning: Artificial Intelligence. Independently Published, 2021.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
10

Art of Feature Engineering: Essentials for Machine Learning. University of Cambridge ESOL Examinations, 2020.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Machine Learning Feature Stores"

1

Consoli, Sergio, Luca Tiozzo Pezzoli, and Elisa Tosetti. "Using the GDELT Dataset to Analyse the Italian Sovereign Bond Market." In Machine Learning, Optimization, and Data Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64583-0_18.

Full text
Abstract:
AbstractThe Global Data on Events, Location, and Tone (GDELT) is a real time large scale database of global human society for open research which monitors worlds broadcast, print, and web news, creating a free open platform for computing on the entire world’s media. In this work, we first describe a data crawler, which collects metadata of the GDELT database in real-time and stores them in a big data management system based on Elasticsearch, a popular and efficient search engine relying on the Lucene library. Then, by exploiting and engineering the detailed information of each news encoded in
APA, Harvard, Vancouver, ISO, and other styles
2

Chabane, Nail, Mohamed Achraf Bouaoune, Reda Amir Sofiane Tighilt, Bogdan Mazoure, Nadia Tahiri, and Vladimir Makarenkov. "Using Clustering and Machine Learning Methods to Provide Intelligent Grocery Shopping Recommendations." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09034-9_10.

Full text
Abstract:
AbstractNowadays, grocery lists make part of shopping habits of many customers. With the popularity of e-commerce and plethora of products and promotions available on online stores, it can become increasingly difficult for customers to identify. products that both satisfy their needs and represent the best deals overall. In this paper, we present a grocery recommender system based on the use of traditional machine learning methods aiming at assisting customers with creation of their grocery lists on the MyGroceryTour platform which displays weekly grocery deals in Canada. Our recommender syste
APA, Harvard, Vancouver, ISO, and other styles
3

Brank, Janez, Dunja Mladenić, Marko Grobelnik, et al. "Feature." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_301.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Zhou, Zhi-Hua. "Feature Selection and Sparse Learning." In Machine Learning. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-1967-3_11.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Faul, A. C. "Feature Learning." In A Concise Introduction to Machine Learning. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781351204750-9.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Jung, Alexander. "Feature Learning." In Machine Learning: Foundations, Methodologies, and Applications. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8193-6_9.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Faul, A. C. "Feature Learning." In A Concise Introduction to Machine Learning, 2nd ed. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003534617-9.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Brank, Janez, Dunja Mladenić, Marko Grobelnik, et al. "Feature Construction." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_302.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Brank, Janez, Dunja Mladenić, Marko Grobelnik, et al. "Feature Extraction." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_304.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Brank, Janez, Dunja Mladenić, Marko Grobelnik, et al. "Feature Reduction." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_305.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Machine Learning Feature Stores"

1

Ramos Nunes, Carlos Eduardo, and Afshin Ashofteh. "A Review of Big Data and Machine Learning Operations in Official Statistics: MLOps and Feature Store Adoption." In 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, 2024. http://dx.doi.org/10.1109/compsac61105.2024.00101.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Barron, Ryan C., Vesselin Grantcharov, Selma Wanna, et al. "Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization." In 2024 International Conference on Machine Learning and Applications (ICMLA). IEEE, 2024. https://doi.org/10.1109/icmla61862.2024.00258.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Aboalanin, Nora Hassan, Mohamed Abo Rizka, and Manar Mohamed Hafez. "Automated Retail Assortment Optimization Using Machine Learning and Deep Learning in Compact Stores During Crisis Periods." In 2024 34th International Conference on Computer Theory and Applications (ICCTA). IEEE, 2024. https://doi.org/10.1109/iccta64612.2024.10974871.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Kanouté, Mamadou, Edith Grall-Maës, and Pierre Beauseroy. "Unsupervised Feature Selection Using Extreme Learning Machine." In 16th International Conference on Neural Computation Theory and Applications. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0013067500003837.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Rathore, Saurabh Pratap Singh, Ali Guma, Sakshi Chamoli, Rayappan Lotus, Yogendra Kumar, and Shailendra Singh Sikarwar. "Machine Learning for Medical Image Feature Extraction." In 2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI). IEEE, 2025. https://doi.org/10.1109/iatmsi64286.2025.10985724.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

S, Kiran Prakash, Keshore T, Surya Prakash B M, and Sathya Selvaraj Sinnasamy. "Feature Driven Property Valuation Using Machine Learning." In 2025 International Conference on Computational Robotics, Testing and Engineering Evaluation (ICCRTEE). IEEE, 2025. https://doi.org/10.1109/iccrtee64519.2025.11053010.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Kamdi, Manasi, Pranay Saraf, Prasad Lokulwar, Chetan Dhule, and Rahul Agrawal. "Feature Extraction of Satellite Images Using Machine Learning." In 2024 International Conference on Innovations and Challenges in Emerging Technologies (ICICET). IEEE, 2024. http://dx.doi.org/10.1109/icicet59348.2024.10616313.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Gupta, Sandeep, Susmith Barigidad, Shadab Hussain, Santosh Dubey, and Sandeep Kanaujia. "Hybrid Machine Learning for Feature-Based Spam Detection." In 2025 2nd International Conference on Computational Intelligence, Communication Technology and Networking (CICTN). IEEE, 2025. https://doi.org/10.1109/cictn64563.2025.10932459.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Lin, Joanne, David Bull, and Nantheera Anantrasirichai. "Enhancing low-light instance segmentation through feature-level denoising." In Machine Learning from Challenging Data 2025, edited by George Sklivanitis, Panagiotis (. Markopoulos, and Bing Ouyang. SPIE, 2025. https://doi.org/10.1117/12.3054001.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Nguyen, Nhi Hoang Tu, Tuan Minh Le, Huy Gia Tran, Tan Duc Tran, Anh Trung Do, and Son Vu Truong Dao. "Fault Diagnosis of Rotating Machine Based on Machine Learning with Feature Selection." In 2024 International Conference on Advanced Technologies for Communications (ATC). IEEE, 2024. https://doi.org/10.1109/atc63255.2024.10908195.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Machine Learning Feature Stores"

1

Fisher, S., and N. McFerran. Nuclear Safeguards: Feature Extraction for Machine Learning Enrichment Analysis of UF6 Cylinders. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1812580.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Junttila, Jukka, Ville Lämsä, Leonardo Espinosa Leal, and Anssi Sillanpää. Feature engineering –based machine learning models for operational state recognition of rotating machines. Peeref, 2023. http://dx.doi.org/10.54985/peeref.2303p8483224.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Faissol, D. M. Creating Feature Representations of Antibody-Antigen Complexes for Fast Binding Prediction with Machine Learning. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1572603.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Murphy, Sean, Mohini Bariya, Debbie Chang, Jeff Lin, Chris Ryan, and Ramiro Mata. Combinatorial Evaluation of Physical Feature Engineering, Classical Machine Learning, and Deep Learning Models for Synchrophasor Data at Scale. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1864556.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Alwan, Iktimal, Dennis D. Spencer, and Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.

Full text
Abstract:
Objective: To compare the performance of popular machine learning algorithms (ML) in mapping the sensorimotor cortex (SM) and identifying the anterior lip of the central sulcus (CS). Methods: We evaluated support vector machines (SVMs), random forest (RF), decision trees (DT), single layer perceptron (SLP), and multilayer perceptron (MLP) against standard logistic regression (LR) to identify the SM cortex employing validated features from six-minute of NREM sleep icEEG data and applying standard common hyperparameters and 10-fold cross-validation. Each algorithm was tested using vetted feature
APA, Harvard, Vancouver, ISO, and other styles
6

Crowe. PR-261-15609-R01 Machine Learning Algorithms for Smart Meter Diagnostics Part II (TR2701). Pipeline Research Council International, Inc. (PRCI), 2015. http://dx.doi.org/10.55274/r0010862.

Full text
Abstract:
Modern smart meters provide an abundance of diagnostic data. Detecting abnormalities in this data can be difficult given the sheer quantity of information. Determining what kind of readings constitute normal operation versus an impending problem has been the subject of significant research; however, there is still room for improvement in real-time fault monitoring. Statistical models known as Machine Learning Algorithms (MLAs) have been identified as a potential solution. A new feature set was selected that allowed for extension of MLAs to ultrasonic meters with different path arrangements. Pr
APA, Harvard, Vancouver, ISO, and other styles
7

Harris, L. B., P. Adiban, and E. Gloaguen. The role of enigmatic deep crustal and upper mantle structures on Au and magmatic Ni-Cu-PGE-Cr mineralization in the Superior Province. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/328984.

Full text
Abstract:
Aeromagnetic and ground gravity data for the Canadian Superior Province, filtered to extract long wavelength components and converted to pseudo-gravity, highlight deep, N-S trending regional-scale, rectilinear faults and margins to discrete, competent mafic or felsic granulite blocks (i.e. at high angles to most regional mapped structures and sub-province boundaries) with little to no surface expression that are spatially associated with lode ('orogenic') Au and Ni-Cu-PGE-Cr occurrences. Statistical and machine learning analysis of the Red Lake-Stormy Lake region in the W Superior Province con
APA, Harvard, Vancouver, ISO, and other styles
8

Crowe, Jeff. PR-261-15609-R02 Machine Learning Algorithms for Smart Meter Diagnostics � Part III (TR2777). Pipeline Research Council International, Inc. (PRCI), 2017. http://dx.doi.org/10.55274/r0011029.

Full text
Abstract:
Our objective of this work was to investigate exclusively Daniel USMs. Sixty five thousand individual data points were used in MLA development which totaled over 18 hours of USM data from seven experimental data sets generated at three flow facilities. Six disturbance types were investigated (baseline, single elbow, double elbow out of plane, liquid, elbow header, and tee header). All experimental data was labeled with the disturbance type, if any, and deviation from baseline error. The MLA feature set was improved from the 2015 work by using gas flow conditions to compare measured and predict
APA, Harvard, Vancouver, ISO, and other styles
9

Pasupuleti, Murali Krishna. Quantum-Enhanced Machine Learning: Harnessing Quantum Computing for Next-Generation AI Systems. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv125.

Full text
Abstract:
Abstract Quantum-enhanced machine learning (QML) represents a paradigm shift in artificial intelligence by integrating quantum computing principles to solve complex computational problems more efficiently than classical methods. By leveraging quantum superposition, entanglement, and parallelism, QML has the potential to accelerate deep learning training, optimize combinatorial problems, and enhance feature selection in high-dimensional spaces. This research explores foundational quantum computing concepts relevant to AI, including quantum circuits, variational quantum algorithms, and quantum k
APA, Harvard, Vancouver, ISO, and other styles
10

Lalisse, Matthias. Measuring the Impact of Campaign Finance on Congressional Voting: A Machine Learning Approach. Institute for New Economic Thinking Working Paper Series, 2022. http://dx.doi.org/10.36687/inetwp178.

Full text
Abstract:
How much does money drive legislative outcomes in the United States? In this article, we use aggregated campaign finance data as well as a Transformer based text embedding model to predict roll call votes for legislation in the US Congress with more than 90% accuracy. In a series of model comparisons in which the input feature sets are varied, we investigate the extent to which campaign finance is predictive of voting behavior in comparison with variables like partisan affiliation. We find that the financial interests backing a legislator’s campaigns are independently predictive in both chambe
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Machine Learning Feature Stores' for particular source types:
Journal articles Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography