Log in

Relevant bibliographies by topics / Decision Tree Algorithm / Books

To see the other types of publications on this topic, follow the link: Decision Tree Algorithm.

Books on the topic 'Decision Tree Algorithm'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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

Select a source type:

Consult the top 25 books for your research on the topic 'Decision Tree Algorithm.'

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

Barros, Rodrigo C., André C. P. L. F. de Carvalho, and Alex A. Freitas. Automatic Design of Decision-Tree Induction Algorithms. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14231-9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Z, Hochberg, ed. Practical algorithms in pediatric endocrinology. Karger, 1999.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

3

L, Bready Lois, Noorily Susan H, and Dillman Dawn, eds. Decision making in anesthesiology: An algorithmic approach. 4th ed. Mosby/Elsevier, 2007.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

4

Shaoul, Ron. Practical algorithms in pediatric gastroenterology. Karger, 2014.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

5

Israel, Zelikovic, and Eisenstein Israel 1964-, eds. Practical algorithms in pediatric nephrology. Karger, 2008.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

6

Khouzam, Nelly. GenID3: A hybrid approach to feature construction in decision trees using genetic algorithms. UMIST, 1997.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

7

ADT 2011 (2011 Piscataway, N.J.). Algorithmic decision theory: Second International Conference, ADT 2011, Piscataway, NJ, USA, October 26-28, 2011 : proceedings. Springer, 2011.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

8

An Algorithm (decision tree) for the management of Parkinson's Disease: Treatment guidelines. Lippincott-Raven, 1998.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

9

Mola, Francesco. Evolutionary Algorithms in Decision Tree Induction. INTECH Open Access Publisher, 2008.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

10

Tree-based Machine Learning Algorithms: Decision Trees, Random Forests, and Boosting. CreateSpace Independent Publishing Platform, 2017.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

11

Grąbczewski, Krzysztof. Meta-Learning in Decision Tree Induction. Springer, 2016.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

12

Grąbczewski, Krzysztof. Meta-Learning in Decision Tree Induction. Springer London, Limited, 2013.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

13

Metalearning In Decision Tree Induction. Springer International Publishing AG, 2013.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

14

Freitas, Alex A., Rodrigo C. C. Barros, and André C.P.L.F de Carvalho. Automatic Design of Decision-Tree Induction Algorithms. Springer, 2015.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

15

Barros, Rodrigo C., Alex A. Freitas, and André C. P. L. F. de Carvalho. Automatic Design of Decision-Tree Induction Algorithms. Springer, 2015.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

16

Bready, Lois L., Susan Helene Noorily, and Dawn Dillman. Decision Making in Anesthesiology. 4th ed. Mosby, 2007.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

17

Williamson, S. Gill, and Edward A. Bender. Mathematics for Algorithm and Systems Analysis. Dover Publications, 2013.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

18

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

19

Sills, Richard H. Practical Algorithms in Pediatric Hematology and Oncology (Practical Algorithms in Pediatrics). S. Karger Publishers (USA), 2003.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

20

Bhatt, Rajen. Fuzzy-Rough Approaches for Pattern Classification: Hybrid Measures, Mathematical Analysis, Feature Selection Algorithms, Decision Tree Algorithms, Neural Learning, and Applications. Independently Published, 2017.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

21

Ramamurthy's Decision Making in Pain Management: An Algorithmic Approach. Jaypee Brothers Medical Publishers, 2018.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

22

Youngberg, Casimira. Machine Learning for Beginners Book : Decision Trees and Random Forests Work: Classification Machine Learning Algorithms. Independently Published, 2021.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

23

Alverio, Taryn. Machine Learning Guide : Instructing Neural Networks, Decision Trees, Random Forest, and Algorithms: Neural Network Definition. Independently Published, 2021.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

24

Machine Learning: The Ultimate Beginners Guide for Neural Networks, Algorithms, Random Forests and Decision Trees Made Simple. CreateSpace Independent Publishing Platform, 2017.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

25

Mooney, Raymond J. Machine Learning. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0020.

Full text

Abstract:

This article introduces the type of symbolic machine learning in which decision trees, rules, or case-based classifiers are induced from supervised training examples. It describes the representation of knowledge assumed by each of these approaches and reviews basic algorithms for inducing such representations from annotated training examples and using the acquired knowledge to classify future instances. Machine learning is the study of computational systems that improve performance on some task with experience. Most machine learning methods concern the task of categorizing examples described b

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!