Machine Analytics has a broad and deep expertise in a range of machine learning techniques and tools along with various optimization methods, such as Expectation Maximization (EM), that are employed in the learning process.

Generative Methods

  • Naïve Bayesian Classifier (NBC)Latent Dirichlet Allocation
  • Hidden Markov Models (HMM) and extensions (Hierarchical, Factorial)
  • Mixture Gaussian models
  • Bayesian Belief Networks
  • Latent Dirichlet Allocation (LDA)
  • Probabilistic Latent Semantic Analysis

Discriminative Methods

  • Linear and Logistic Regression
  • Support Vector Machine (SVM)
  • Artificial Neural Networks
  • Nearest Neighbor

Clustering Methods

  • Subspace methods (SVD, LSA, PCA, ICA), SVM
  • Hierarchical and k-Means

Deep Learning

  • Restricted Boltzmann Machine (RBM) and Deep Belief Network (DBN)
  • Convolutional Neural Network (CNN)
  • Stacked Auto-Encoders
  • Deep Recursive Neural Network (RNN)
  • Deep Fusion: Application of above deep learning techniques to solve data fusion problems.