What's New

Learn about new product capabilities.

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.

Version 10.0, part of Release 2015a, includes the following enhancements:

  • Classification app to train models and classify data using supervised machine learning
  • Statistical tests for comparing accuracies of two classification models using compareHoldout, testcholdout, and testckfold functions
  • Speedup of kmedoids, fitcknn, and other functions when using cosine, correlation, or spearman distance calculations
  • Performance enhancements for decision trees and performance curves​​
  • Additional option to control decision tree depth using 'MaxNumSplits' argument in fitctree, fitrtree, and templateTree functions
  • Code generation for pca and probability distribution functions (using MATLAB Coder)
  • Power and sample size for two-sample t-test using sampsizepwr function

See the Release Notes for details.

Version 9.1, part of Release 2014b, includes the following enhancements:

  • Multiclass learning for support vector machines and other classifiers using the fitcecoc function
  • Generalized linear mixed-effects models using the fitglme function
  • Clustering that is robust to outliers using the kmedoids function
  • Speedup of the kmeans and gmdistribution clustering using the kmeans++ algorithm
  • Fisher's exact test for 2-by-2 contingency tables

See the Release Notes for details.