Log in

Relevant bibliographies by topics / Supervised and unsupervised learning / Books

To see the other types of publications on this topic, follow the link: Supervised and unsupervised learning.

Books on the topic 'Supervised and unsupervised learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 20 June 2025

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

Select a source type:

Consult the top 50 books for your research on the topic 'Supervised and unsupervised learning.'

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.

Browse books on a wide variety of disciplines and organise your bibliography correctly.

1

Albalate, Amparo, and Wolfgang Minker. Semi-Supervised and Unsupervised Machine Learning. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118557693.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Berry, Michael W., Azlinah Mohamed, and Bee Wah Yap, eds. Supervised and Unsupervised Learning for Data Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-22475-2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Kyan, Matthew, Paisarn Muneesawang, Kambiz Jarrah, and Ling Guan. Unsupervised Learning. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118875568.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Ros, Frédéric, and Serge Guillaume, eds. Sampling Techniques for Supervised or Unsupervised Tasks. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-29349-9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Okun, Oleg, and Giorgio Valentini, eds. Applications of Supervised and Unsupervised Ensemble Methods. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03999-7.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Acuña, Ana Isabel González. Contributions to unsupervised and supervised learning with applications in digital image processing: Dissertation presented to the Department Of Computer Science and Artificial Intelligence in partial fulfillment of the requeriments for the degree of Doctor of Philosophy. Universidad del País Vasco, Servicio Editorial = Euskal Herriko Unibertsitatea, Argitalpen Zerbitzua, 2012.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

7

Okun, Oleg, and Giorgio Valentini, eds. Supervised and Unsupervised Ensemble Methods and their Applications. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78981-9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Celebi, M. Emre, and Kemal Aydin, eds. Unsupervised Learning Algorithms. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24211-8.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Schwenker, Friedhelm, and Edmondo Trentin, eds. Partially Supervised Learning. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28258-4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Zhou, Zhi-Hua, and Friedhelm Schwenker, eds. Partially Supervised Learning. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40705-5.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Agne, Michael Robert. An Assortment of Unsupervised and Supervised Applications to Large Data. [publisher not identified], 2015.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

12

Cuzzolin, Fabio, Kevin Cannons, and Vincenzo Lomonaco, eds. Continual Semi-Supervised Learning. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17587-9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Verdhan, Vaibhav. Supervised Learning with Python. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6156-9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Li, Xiangtao, and Ka-Chun Wong, eds. Natural Computing for Unsupervised Learning. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-98566-4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Zhu, Xiaojin, and Andrew B. Goldberg. Introduction to Semi-Supervised Learning. Springer International Publishing, 2009. http://dx.doi.org/10.1007/978-3-031-01548-9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Subramanya, Amarnag, and Partha Pratim Talukdar. Graph-Based Semi-Supervised Learning. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-031-01571-7.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Schuld, Maria, and Francesco Petruccione. Supervised Learning with Quantum Computers. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96424-9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Cerulli, Giovanni. Fundamentals of Supervised Machine Learning. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-41337-7.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Selle, Stefan. Data Science Training - Supervised Learning. Springer Berlin Heidelberg, 2024. https://doi.org/10.1007/978-3-662-67960-9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Leordeanu, Marius. Unsupervised Learning in Space and Time. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42128-1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Jo, Taeho. Machine Learning Foundations: Supervised, Unsupervised, and Advanced Learning. Springer International Publishing AG, 2022.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

22

Jo, Taeho. Machine Learning Foundations: Supervised, Unsupervised, and Advanced Learning. Springer International Publishing AG, 2021.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

23

Berry, Michael W., Bee Wah Yap, and Azlinah Mohamed. Supervised and Unsupervised Learning for Data Science. Springer, 2019.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

24

Berry, Michael W., Bee Wah Yap, and Azlinah Mohamed. Supervised and Unsupervised Learning for Data Science. Springer International Publishing AG, 2020.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

25

Colins, Michael. Machine Learning: An Introduction To Supervised & Unsupervised Learning Algorithms. Createspace Independent Publishing Platform, 2017.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

26

Applications Of Supervised And Unsupervised Ensemble Methods. Springer, 2009.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

27

Sage, Anderson. Machine Learning Foundation: An Introduction to Supervised and Unsupervised Learning. Independently Published, 2022.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

28

Okun, Oleg. Applications of Supervised and Unsupervised Ensemble Methods. Springer, 2009.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

29

Okun, Oleg. Applications of Supervised and Unsupervised Ensemble Methods. Springer Berlin / Heidelberg, 2012.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

30

Okun, Oleg. Supervised and Unsupervised Ensemble Methods and Their Applications. Springer London, Limited, 2008.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

31

Okun, Oleg. Supervised and Unsupervised Ensemble Methods and their Applications. Springer, 2010.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

32

Huang, Te-Ming, Vojislav Kecman, and Ivica Kopriva. Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-Supervised, and Unsupervised Learning. Springer London, Limited, 2006.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

33

Huang, Te-Ming, Vojislav Kecman, and Ivica Kopriva. Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning. Springer, 2010.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

34

Machine learning Beginners Guide Algorithms: Supervised & Unsupervised learning, Decision Tree & Random Forest Introduction. CreateSpace Independent Publishing Platform, 2017.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

35

Kshatri, 1st Sapna Singh, 2nd Devanand Bhonsle, Roshni Rahangdale Ms 3rd, Tanu Rizvi Ms IV, and V. Ruhi uzma Sheikh. Supervised and Unsupervised Machine Learning Methods and Their Crime Data Applications. INSC International Publisher (IIP), 2022.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

36

Herreros, Ivan. Learning and control. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0026.

Full text

Abstract:

This chapter discusses basic concepts from control theory and machine learning to facilitate a formal understanding of animal learning and motor control. It first distinguishes between feedback and feed-forward control strategies, and later introduces the classification of machine learning applications into supervised, unsupervised, and reinforcement learning problems. Next, it links these concepts with their counterparts in the domain of the psychology of animal learning, highlighting the analogies between supervised learning and classical conditioning, reinforcement learning and operant conditioning, and between unsupervised and perceptual learning. Additionally, it interprets innate and acquired actions from the standpoint of feedback vs anticipatory and adaptive control. Finally, it argues how this framework of translating knowledge between formal and biological disciplines can serve us to not only structure and advance our understanding of brain function but also enrich engineering solutions at the level of robot learning and control with insights coming from biology.

APA, Harvard, Vancouver, ISO, and other styles

37

Huang, Te-Ming, Vojislav Kecman, and Ivica Kopriva. Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning (Studies in Computational Intelligence). Springer, 2006.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

38

Chinnamgari, Sunil Kumar. R Machine Learning Projects: Implement Supervised, Unsupervised, and Reinforcement Learning Techniques Using R 3. 5. Packt Publishing, Limited, 2019.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

39

Pal, Sujit, Amita Kapoor, Antonio Gulli, and François Chollet. Deep Learning with TensorFlow and Keras: Build and Deploy Supervised, Unsupervised, Deep, and Reinforcement Learning Models. Packt Publishing, Limited, 2022.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

40

FISHER, Terry. Paperback - a Practical Guide to Implementing Supervised and Unsupervised Machine Learning Algorithms in Python. Independently Published, 2021.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

41

Dangeti, Pratap. Statistics for Machine Learning: Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R. Packt Publishing, 2017.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

42

Statistics for Machine Learning: Techniques for Exploring Supervised, Unsupervised, and Reinforcement Learning Models with Python and R. de Gruyter GmbH, Walter, 2017.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

43

Unsupervised and Weakly-Supervised Learning of Localized Texture Patterns of Lung Diseases on Computed Tomography. [publisher not identified], 2019.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

44

Bironneau, Michael, and Toby Coleman. Machine Learning with Go Quick Start Guide: Hands-On Techniques for Building Supervised and Unsupervised Machine Learning Workflows. Packt Publishing, Limited, 2019.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

45

Deep Learning with TensorFlow and Keras - 3rd Edition: Build and Deploy Supervised, Unsupervised, Deep, and Reinforcement Learning Models. de Gruyter GmbH, Walter, 2022.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

46

Amr, Tarek. Hands-On Machine Learning with Scikit-learn and Scientific Python Toolkits: A Practical Guide to Implementing Supervised and Unsupervised Machine Learning Algorithms in Python. Packt Publishing, Limited, 2020.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

47

Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits: A practical guide to implementing supervised and unsupervised machine learning algorithms in Python. Packt Publishing, 2020.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

48

Rajakumar, P. S., S. Geetha, and T. V. Ananthan. Fundamentals of Image Processing. Jupiter Publications Consortium, 2023. http://dx.doi.org/10.47715/jpc.b.978-93-91303-80-8.

Full text

Abstract:

"Fundamentals of Image Processing" offers a comprehensive exploration of image processing's pivotal techniques, tools, and applications. Beginning with an overview, the book systematically categorizes and explains the multifaceted steps and methodologies inherent to the digital processing of images. The text progresses from basic concepts like sampling and quantization to advanced techniques such as image restoration and feature extraction. Special emphasis is given to algorithms and models crucial to image enhancement, restoration, segmentation, and application. In the initial segments, the intricacies of digital imaging systems, pixel connectivity, color models, and file formats are dissected. Following this, image enhancement techniques, including spatial and frequency domain methods and histogram processing, are elaborated upon. The book then addresses image restoration, discussing degradation models, noise modeling, and blur, and offers insights into the compelling world of multi-resolution analysis with in-depth discussions on wavelets and image pyramids. Segmentation processes, especially edge operators, boundary detections, and thresholding techniques, are detailed in subsequent chapters. The text culminates by diving deep into the applications of image processing, exploring supervised and unsupervised learning, clustering algorithms, and various classifiers. Throughout the discourse, practical examples, real-world applications, and intuitive diagrams are integrated to facilitate an enriched learning experience. This book stands as an essential guide for both novices aiming to grasp the basics and experts looking to hone their knowledge in image processing. Keywords: Digital Imaging Systems, Image Enhancement, Image Restoration, Multi-resolution Analysis, Wavelets, Image Segmentation, Feature Extraction, SIFT, SURF, Image Classifiers, Supervised Learning, Clustering Algorithms.

APA, Harvard, Vancouver, ISO, and other styles

49

Micheli-Tzanakou, Evangelia, ed. Supervised and Unsupervised Pattern Recognition. CRC Press, 1999. http://dx.doi.org/10.1201/9781420049770.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Hinton, Geoffrey, and Terrence J. Sejnowski, eds. Unsupervised Learning. The MIT Press, 1999. http://dx.doi.org/10.7551/mitpress/7011.001.0001.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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!