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Books on the topic 'Machine Learning Feature Stores'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

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

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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.

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3

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

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4

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

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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.

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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

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6

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

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7

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

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8

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

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9

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

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10

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

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11

Feature Engineering for Machine Learning and Data Analytics. Taylor & Francis Group, 2018.

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12

Aithal, Bharath, and Prakash P. S. Building Feature Extraction with Machine Learning: Geospatial Applications. Taylor & Francis Group, 2022.

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13

Aithal, Bharath, and Prakash P. S. Building Feature Extraction with Machine Learning: Geospatial Applications. CRC Press LLC, 2022.

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14

Dong, Guozhu, and Huan Liu. Feature Engineering for Machine Learning and Data Analytics. Taylor & Francis Group, 2018.

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15

Dong, Guozhu, and Huan Liu. Feature Engineering for Machine Learning and Data Analytics. Taylor & Francis Group, 2018.

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16

Aithal, Bharath, and Prakash P. S. Building Feature Extraction with Machine Learning: Geospatial Applications. Taylor & Francis Group, 2022.

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17

Dong, Guozhu, and Huan Liu. Feature Engineering for Machine Learning and Data Analytics. Taylor & Francis Group, 2020.

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18

Club, Rocket Baby. Party Parrots : Machine Learning for Kids: Feature Engineering. Primedia eLaunch LLC, 2018.

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19

Dong, Guozhu, and Huan Liu. Feature Engineering for Machine Learning and Data Analytics. Taylor & Francis Group, 2018.

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20

Dong, Guozhu, and Huan Liu. Feature Engineering for Machine Learning and Data Analytics. Taylor & Francis Group, 2018.

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21

S, Prakash P. Building Feature Extraction with Machine Learning: Geospatial Applications. CRC Press LLC, 2022.

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22

Kim, Kwangjo, Muhamad Erza Aminanto, and Harry Chandra Tanuwidjaja. Network Intrusion Detection using Deep Learning: A Feature Learning Approach. Springer, 2018.

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23

Kim, Kwangjo, Muhamad Erza Aminanto, and Harry Chandra Tanuwidjaja. Network Intrusion Detection using Deep Learning: A Feature Learning Approach. Springer, 2018.

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24

Computational methods of feature selection. Chapman & Hall/CRC, 2008.

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25

Kumar, 1st Neelam Sanjeev, 2nd P. Nirmalkumar, and 3rd Kareemullah H. Feature Extraction and Classification in Context of Machine Learning. INSC International Publisher (IIP), 2021.

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26

Zhang, Chuanrong, ed. Feature Papers for Land Innovations – Data and Machine Learning. MDPI, 2024. http://dx.doi.org/10.3390/books978-3-7258-0084-1.

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27

Anthony Mihirana Mihirana De Silva and Philip H. W. Leong. Grammar-Based Feature Generation for Time-Series Prediction. Springer, 2015.

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28

Leong, Philip H. W., and Anthony Mihirana De Silva. Grammar-Based Feature Generation for Time-Series Prediction. Springer London, Limited, 2015.

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29

Hinders, Mark K. Intelligent Feature Selection for Machine Learning Using the Dynamic Wavelet Fingerprint. Springer International Publishing AG, 2021.

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30

Hinders, Mark K. Intelligent Feature Selection for Machine Learning Using the Dynamic Wavelet Fingerprint. Springer International Publishing AG, 2020.

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31

Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists. O'Reilly Media, 2018.

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32

Misra, Harekrishna, and Mrutyunjaya Panda. Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science. IGI Global, 2021.

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33

Misra, Harekrishna, and Mrutyunjaya Panda. Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science. IGI Global, 2021.

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34

Misra, Harekrishna, and Mrutyunjaya Panda. Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science. IGI Global, 2021.

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35

Misra, Harekrishna, and Mrutyunjaya Panda. Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science. IGI Global, 2021.

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36

Das, Rik. Content-Based Image Classification: Efficient Machine Learning Using Robust Feature Extraction Techniques. Taylor & Francis Group, 2020.

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37

Content-Based Image Classification: Efficient Machine Learning Using Robust Feature Extraction Techniques. Taylor & Francis Group, 2020.

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38

Das, Rik. Content-Based Image Classification: Efficient Machine Learning Using Robust Feature Extraction Techniques. Taylor & Francis Group, 2020.

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39

Das, Rik. Content-Based Image Classification: Efficient Machine Learning Using Robust Feature Extraction Techniques. Taylor & Francis Group, 2020.

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40

Machine Learning for Mass Production and Industrial Engineering. Logos-Verlag Berlin, 2007.

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41

Sundararajan, Srikrishnan. Multivariate Analysis and Machine Learning Techniques: Feature Analysis in Data Science Using Python. Springer, 2023.

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42

Jensen, Richard, and Qiang Shen. Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches. Wiley & Sons, Incorporated, John, 2008.

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43

Jensen, Richard, and Qiang Shen. Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches. Wiley & Sons, Incorporated, John, 2008.

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44

Ozdemir, Sinan, and Divya Susarla. Feature Engineering Made Easy: Identify unique features from your dataset in order to build powerful machine learning systems. Packt Publishing, 2018.

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45

Python Feature Engineering Cookbook: Over 70 Recipes for Creating, Engineering, and Transforming Features to Build Machine Learning Models. de Gruyter GmbH, Walter, 2022.

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46

Galli, Soledad. Python Feature Engineering Cookbook: Over 70 recipes for creating, engineering, and transforming features to build machine learning models. Packt Publishing, 2020.

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47

Feature Engineering Made Easy: Identify Unique Features from Your Dataset in Order to Build Powerful Machine Learning Systems. de Gruyter GmbH, Walter, 2018.

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48

Galli, Soledad. Python Feature Engineering Cookbook: Over 70 Recipes for Creating, Engineering, and Transforming Features to Build Machine Learning Models, 2nd Edition. Packt Publishing, Limited, 2022.

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49

(Editor), Huan Liu, and Hiroshi Motoda (Editor), eds. Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series). Chapman & Hall/CRC, 2007.

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50

Leibo, Joel Z., and Tomaso Poggio. Perception. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0025.

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This chapter provides an overview of biological perceptual systems and their underlying computational principles focusing on the sensory sheets of the retina and cochlea and exploring how complex feature detection emerges by combining simple feature detectors in a hierarchical fashion. We also explore how the microcircuits of the neocortex implement such schemes pointing out similarities to progress in the field of machine vision driven deep learning algorithms. We see signs that engineered systems are catching up with the brain. For example, vision-based pedestrian detection systems are now a

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