Special Issue "Regularization Techniques for Machine Learning and Their Applications"
Deadline for manuscript submissions: 31 December 2020.
Interests: mobile agents; WSN routing algorithms; medical informatics; artificial intelligence; E-learning
Interests: artificial neural networks; numerical analysis; computational mathematics; machine learning; algorithms; semi-supervised learning; ICT in education; data mining; deep learning
Interests: web information management; internet technologies and web applications; communication networks; multimedia retrieval; personalisation / adaptation; social networking and integrated services; E-Commerce; E-Learning; intelligent systems; aplications in bioinformatics
We invite you to submit your latest research in the development of ensemble algorithms to this Special Issue, “Regularization Techniques for Machine Learning and Their Applications”.
Over the last decade, learning theory has led to the achievement of significant progress in the development of sophisticated algorithms and their theoretical foundations. The theory builds on concepts which exploit ideas and methodologies from mathematical areas, such as optimization theory. Regularization is probably the key to address the challenging problem of overfitting, which usually occurs in high-dimensional learning. Its primary goal is to make the machine learning algorithm “learn” and not “memorize” by penalizing the algorithm to reduce its generalization error in order to avoid the risk of overfitting. As a result, the variance of the model is significantly reduced, without substantial increase in its bias and without losing any important properties in the data.
The main aim of this Special Issue is to present the recent advances related to all kinds of regularization methodologies and investigations of the impact of their application to a diversity of real-world problems.
Prof. Dr. Theodore Kotsilieris
Dr. Ioannis E. Livieris
Prof. Dr. Ioannis Anagnostopoulos
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Regularized neural networks
- Dropout & Dropconnect techniques
- Regularization for deep learning models
- Weight-constrained neural networks
- L-norm regularization
- Adversarial learning
- Penalty functions
- Multitask learning
- Pooling techniques
- Model selection techniques
- Matrix regularizers
- Data augmentation
- Early stopping strategies