IJMLTA is being launched by ACORS. Its ISSN is awaited; submissions on machine learning techniques and applications are welcome.
About the journal
IJMLTA is a peer-reviewed journal in which every article is reviewed by several experts in the field. Upon acceptance, articles are published in the latest open volume. Like IJACOI, it follows the continuous, open-access model with rapid peer review, no colour or page charges and free submission.
Scope
The journal welcomes original research and review papers on machine learning techniques (Track 1) and their applications (Track 2), including:
- Linear Regression
- Logistic Regression
- Decision Tree
- Support Vector Machine
- Naive Bayes
- K-Nearest Neighbour
- Random Forest
- Dimensionality Reduction
- Gradient Boosting
- Self-Organizing Maps
- Multivariate Adaptive Regression
- Neural Networks
- Hidden Markov Models
- Gaussian Mixtures
- Discriminant Analysis
- Fuzzy C-means
- K-Medoids
Questions: info@acors.org