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Supported Features


"If you're not sure whether DynaML fits your requirements, this list provides a semi-comprehensive overview of available features."


Model Family Supported Notes
Generalized Linear Models Yes Supports regularized least squares based models for regression as well as logistic and probit models for classification.
Generalized Least Squares Models Yes -
Least Squares Support Vector Machines Yes Contains implementation of dual LS-SVM applied to classification and regression.
Gaussian Processes Yes Supports gaussian process inference models for regression and binary classification; the binary classification GP implementation uses the Laplace approximation for posterior mode computation. For regression problems, there are also multi-output and multi-task GP implementations.
Student T Processes Yes Supports student T process inference models for regression, there are also multi-output and multi-task STP implementations.
Multi-output Matrix T Process Yes _
Skew Gaussian Processes Yes Supports extended skew gaussian process inference models for regression.
Feed forward Neural Networks Yes Can build and learn feedforward neural nets of various sizes.
Committee/Meta Models Yes Supports creation of gating networks or committee models.

Optimizers & Solvers

Parametric Solvers

Solver Supported Notes
Regularized Least Squares Yes Solves the Tikhonov Regularization problem exactly (not suitable for large data sets)
Gradient Descent Yes Stochastic and batch gradient descent is implemented.
Quasi-Newton BFGS Yes Second order convex optimization (using Hessian).
Conjugate Gradient Yes Supports solving of linear systems of type A.x = b where A is a symmetric positive definite matrix.
Committee Model Solver Yes Solves any committee based model to calculate member model coefficients or confidences.
Back-propagation Yes Least squares based back-propagation with momentum and regularization.

Global Optimization Solvers

Solver Supported Notes
Grid Search Yes Simple search over a grid of configurations.
Coupled Simulated Annealing Yes Supports vanilla (simulated annealing) along with variants of CSA such as CSA with variance (temperature) control.
ML-II Yes Gradient based optimization of log marginal likelihood in Gaussian Process regression models.