Machine Learning Algorithms

Machine learning algorithms cannot be completely preprogrammed and fixed in advance because application contexts can vary greatly. Instead, a broad family of algorithms is selected for a given situation and their variable parameters are tuned (learned) to fit a specific application’s data.

There are many useful architectures in machine learning. Some of those that our team has used in various projects include:

  • Artificial neural networks
  • Bayesian networks
  • Support vector machines
  • Radial basis function networks
  • Self-organizing (Kohonen) maps
  • Probabilistic and clustering trees
  • Evolutionary and genetic algorithms
  • Fuzzy logic and neuro-fuzzy machines

Sample Projects

For nearly two decades, the NeurOK Software team has been developing and deploying machine learning-based solutions in the following areas:

  • Modeling
  • Optimization
  • Signal Processing
  • Non-linear Control
  • Time Series Analysis
  • Clustering and Visualization
  • Data/Text Mining and Classification