Skip to content
Free planNo credit card requiredStart writing
AI for Research home
AI for Research home
Dashboard
Sign inGet started

Search 250 million research papers

Read the abstract, see who wrote it and who cites it, and get the citation in APA, MLA, Chicago or Harvard.

About 10,000 papers for “Machine learning”

Machine learning vs. neutrosophic machine learning

Shabbir Hassan · 2025

Machine Learning (ML) and Neutrosophic Machine Learning (NML) represent two distinct paradigms in the realm of artificial intelligence, each addressing unique challenges in data processing, decision-making, and uncertainty modeling. ML, rooted in classical statistical and probabilistic principles, excels in tasks ranging from classification to recommendation systems. Its strength lies in the ability to discern patterns from labeled data and make predictions based on learned relationships. However, ML faces limitations in handling complex uncertainties beyond probabilistic representations. On the other hand, NML extends the ML framework by incorporating neutrosophic sets and logic. This introduces a third truth value, allowing for the explicit representation of truth, indeterminacy, and falsity within the data. NML is particularly adept at handling uncertainties, vagueness, and inconsistencies in various applications, such as medical diagnosis, robotics, and decision support systems. While ML models may lack interpretability, NML provides a more structured and transparent framework, offering insights into the decision-making process, especially in subjective and uncertain domains. The comparison between ML and NML involves trade-offs. ML, with its versatility and widespread adoption, is effective in tasks where uncertainties are primarily probabilistic. NML, with its focus on explicit uncertainty representation, is tailored for applications where indeterminacy and vagueness play a pivotal role. Understanding the strengths and limitations of each paradigm is crucial in selecting the appropriate approach based on the specific requirements of a given task. This chapter provides a deep insight into ML and NML and compares them with relevant parameters.

Cited by 0

Learning machine learning

Teng, Jin Qi · DR-NTU (Nanyang Technological University) · 2023

This project aims to propose a one-stop learning platform for machine learning (ml): the “SALTMODA” web application. Machine learning itself is a complex subject matter that intimidates society from getting themselves associated with it. The goal of this project is to simplify and enhance a user’s course of education on ml through helping to break down the chosen ml content into simpler explanations, forming a community where people can discover others on the same learning journey and provide a platform for users to attain or be directed to the support that they require in their education. This report contains details related to the SALTMODA web application from its development to its execution. Finally, the report will also include a conclusion and reflection of the process of the development of SALTMODA. This project is a step towards the direction of convincing people to not shy away from the topic ml as it growing becomes more implemented and integrated into our daily lives.

Cited by 0

Learning machine learning

Chua, Beng Choon · DR-NTU (Nanyang Technological University) · 2023

Machine learning is a rapidly advancing field that enables computers to learn from
\ndata without explicit programming. Despite its potential for solving complex problems
\nand achieving state-of-the-art performance, novice learners, including secondary
\nschool students may find the fundamental concepts and theories of machine learning
\nintimidating, which could discourage them from learning it.
\nCurrently, there is a lack of educational tools and research for teaching machine
\nlearning to this demographic. As such, this project aims to provide a comprehensive
\nintroduction to basic machine learning concepts, with a focus on the problem of image
\nclassification. The web application will make use of HTML, CSS, JavaScript, and
\nTensorFlow.js to include explanations of basic concepts, visual and interactive
\nexamples of relevant algorithms such as K-Nearest Neighbors and Neural Networks
\nand guided live classification activities. Additionally, quizzes will also be provided to
\nhelp learners reinforce their understanding in the topics that they have just learnt.

Cited by 0

Learning machine learning

Ted G. Lewis, Peter J. Denning · Communications of the ACM · 2018

A discussion of the rapidly evolving realm of machine learning.

Cited by 14

Machine Learning using MapReduceWhat is Machine Learning

2013

◮ Machine learning is a subfield of artificial intelligence concerned with techniques that allow computers to improve their outputs based on previous experiences (stored as data).What is Machine Learning ◮ Machine learning is a subfield of artificial intelligence concerned with techniques that allow computers to improve their outputs based on previous experiences (stored as data). ◮ Closely related to data mining and often uses techniques from statistics, probability theory, pattern recognition, and a host of other areas. What is Machine Learning ◮ Machine learning is a subfield of artificial intelligence concerned with techniques that allow computers to improve their outputs based on previous experiences (stored as data). ◮ Closely related to data mining and often uses techniques from statistics, probability theory, pattern recognition, and a host of other areas. ◮ “More data usually beats better algorithms. ” Anand Rajaraman. What is Machine Learning ◮ Machine learning is a subfield of artificial intelligence concerned with techniques that allow computers to improve their outputs based on previous experiences (stored as data). ◮ Closely related to data mining and often uses techniques from statistics, probability theory, pattern recognition, and a host of other areas. ◮ “More data usually beats better algorithms. ” Anand Rajaraman. ◮ Sample uses of machine learning:

Cited by 0

Machine Learning

Marcel Kollovieh, David Bani-Harouni · Der Hautarzt · 2021

Cited by 1Open access

Machine Learning

Leyrit, L., Chateau, T., Lapresté, J.T. · HAL (Le Centre pour la Communication Scientifique Directe) · 2010

Cited by 0

Machine Learning

William F. Schneider (1487236), Hua Guo (305546) · Figshare · 2018

Cited by 0Open access

Machine learning.

Rui Guo (134395), Qirui Zheng (20793416), Liang Zhang (41392) · Figshare · 2025

(A-B) Biomarkers was using the SVMs through 5-fold cross-validation. (C-D) LASSO logistic regression algorithm to screen diagnostic markers. (E) Based on RF algorithm to screen biomarkers. (F) Venn diagram shows that 6 genes are identified from three ML algorithms. SVM-RFE support vector machine-recursive feature elimination, RF random forest, LASSO least absolute shrinkage and selection operator. ML Machine Learning.

Cited by 0Open access

Machine learning

van den Bosch, A., Lüdeling, A., Kytö, M. · Data Archiving and Networked Services (DANS) · 2009

Cited by 0

Machine Learning

Mellouk, A, Chebira, A. · HAL (Le Centre pour la Communication Scientifique Directe) · 2009

International audience

Cited by 0

Machine learning

Srinu, S · UNAM Scholarly Repository (University of Namibia) · 2025

Cited by 0

Machine Learning

Jaakkola, Tommi S. (Tommi Sakari) · DSpace@MIT (Massachusetts Institute of Technology) · 2002

Principles, techniques, and algorithms in machine learning from the point of view of statistical inference; representation, generalization, and model selection; and methods such as linear/additive models, active learning, boosting, support vector machines, hidden Markov models, and Bayesian networks. From the course home page: Course Description 6.867 is an introductory course on machine learning which provides an overview of many techniques and algorithms in machine learning, beginning with topics such as simple perceptrons and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course gives the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how and why they work. The underlying theme in the course is statistical inference as this provides the foundation for most of the methods covered.

Cited by 0Open access