Version 11.2, part of Release 2017b, includes the following enhancements:

  • Code Generation: Generate C code for prediction by using discriminant analysis, k-nearest neighbor, SVM regression, regression tree ensemble, and Gaussian process regression models (requires MATLAB Coder)
  • Big Data Algorithms: Fit kernel SVM classification models by using random feature expansion, fit linear SVM regression models, grow decision trees, and draw weighted random samples from out-of-memory data
  • Parallel Bayesian Optimization: Tune hyperparameters faster by using parallel function evaluation (requires Parallel Computing Toolbox)
  • Machine Learning Apps: Select training data more efficiently in the Classification Learner and Regression Learner Apps​
  • Partial Dependence Plots: Visualize relationships between features and predicted responses through marginalization

See the Release Notes for details.

Version 11.1, part of Release 2017a, includes the following enhancements:

  • Regression Learner App: Train regression models using supervised machine learning
  • Big Data Algorithms: Perform support vector machine (SVM) and Naive Bayes classification, create bags of decision trees, and fit lasso regression on out-of-memory data
  • Code Generation: Generate C code for prediction by using linear models, generalized linear models, decision trees, and ensembles of classification trees (requires MATLAB Coder)
  • Bayesian Statistics: Perform gradient-based sampling using Hamiltonian Monte Carlo (HMC) sampler
  • Feature Extraction: Perform unsupervised feature learning by using sparse filtering and reconstruction independent component analysis (RICA)

See the Release Notes for details.

Version 11.0, part of Release 2016b, includes the following enhancements:

  • Big Data Algorithms: Perform dimension reduction, descriptive statistics, k-means clustering, linear regression, logistic regression, and discriminant analysis on out-of-memory data
  • Bayesian Optimization: Tune machine learning algorithms by searching for optimal hyperparameters
  • Feature Selection: Use neighborhood component analysis (NCA) to choose features for machine learning models
  • Code Generation: Generate C code for prediction by using SVM and logistic regression models (requires MATLAB Coder)
  • Classification Learner: Train classifiers in parallel (requires Parallel Computing Toolbox)
  • Machine Learning Performance: Speed up Gaussian mixture modeling, SVM with duplicate observations, and distance calculations for sparse data
  • Survival Analysis: Fit Cox proportional hazards models with new options for residuals and handling ties

See the Release Notes for details.

Version 10.2, part of Release 2016a, includes the following enhancements:

  • Machine Learning for High-Dimensional Data: Perform fast fitting of linear classification and regression models with techniques such as stochastic gradient descent and (L)BFGS using fitclinear and fitrlinear functions
  • Classification Learner: Train multiple models automatically, visualize results by class labels, and perform logistic regression classification
  • Performance: Perform clustering using kmeans, kmedoids, and Gaussian mixture models faster when data has a large number of clusters
  • Probability Distributions: Fit kernel smoothing density to multivariate data using the ksdensity and mvksdensity functions
  • Stable Distributions: Model financial and other data that requires heavy-tailed distributions

See the Release Notes for details.

Version 10.1, part of Release 2015b, includes the following enhancements:

  • Classification Learner: Train discriminant analysis to classify data, train models using categorical predictors, and perform dimensionality reduction using PCA
  • Nonparametric Regression: Fit models using support vector regression (SVR) or Gaussian processes (Kriging)​
  • Tables and Categorical Data for Machine Learning: Use table and categorical predictors in classification and nonparametric regression functions and in Classification Learner​
  • Code Generation: Automatically generate C and C++ code for kmeans and randsample functions (using MATLAB Coder)​
  • GPU Acceleration: Speed up computation for over 65 functions including probability distributions, descriptive statistics, and hypothesis testing (using Parallel Computing Toolbox)

See the Release Notes for details.