Open Journal of Machine Learning

 

Machine learning extends its wings to every modern technology. The intelligence that these learning systems add to our every-day-tasks optimizes and personalises our online services, opening also new possibilities to the way we create and communicate. The aim of the Open Journal of Machine Learning is to present new and original research methods on machine learning or applications of these methods, and a platform to discuss future steps on this area.

This journal is open access only, keeping the authors the copyright of the paper under Creative Commons license. Currently, its publications fees are waived for every paper submitted before January 2021.

The main research areas of the journal include, but are not limited to:

  • Classification
  • Clustering
  • Deep Learning
  • Statistical inference
  • Natural Language Processing
  • Reinforcement Learning
  • Multi-Agent System
  • Bio-Inspired Algorithms
  • Monte Carlo Methods
  • Markov Models
  • Social Network Analysis
  • Forecasting
  • Time-series analysis
  • Image segmentation
  • Profiling and Behavioral models
  • Applications of Machine Learning
  • The Security of Machine Learning
  • Adversarial Machine Learning
  • Hardware components for Machine Learning

For more information about submission and templates, please check the Authors Guidelines.