Algorithms For Pattern Recognition II

Ensemble learning algorithms (supervised meta-algorithms for combining multiple learning algorithms together)

Here are some algorithm that based on Ensemble learning algorithms:

• Boosting (meta-algorithm)
• Bootstrap aggregating ("bagging")
• Ensemble averaging
• Mixture of experts, hierarchical mixture of experts
• General algorithms for predicting arbitrarily-structured (sets of) labels
• Bayesian networks
• Markov random fields

Multilinear subspace learning algorithms (predicting labels of multidimensional data using tensor representations)

Unsupervised:

• Multilinear principal component analysis (MPCA)
• Real-valued sequence labeling algorithms (predicting sequences of real-valued labels)

Supervised :

• Kalman filters
• Particle filters
• Regression algorithms (predicting real-valued labels)

Regression Algorithms

Supervised:

• Gaussian process regression (kriging)
• Linear regression and extensions
• Neural networks and Deep learning methods

Unsupervised:

• Independent component analysis (ICA)
• Principal components analysis (PCA)

Sequence labeling algorithms (predicting sequences of categorical labels)

Supervised:

• Conditional random fields (CRFs)
• Hidden Markov models (HMMs)
• Maximum entropy Markov models (MEMMs)
• Recurrent neural networks

Unsupervised:

• Hidden Markov models (HMMs)