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statics and manchine learning toolbox release note.pdf
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The software described in this document is furnished under a license agreement. The software may be used or copied only under the terms of the license agreement. No part of this manual may be photocopied or reproduced in any form without prior written consent from The MathWorks, Inc.
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Statistics and Machine Learning Toolbox™ Release
Notes
How to Contact MathWorks
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The MathWorks, Inc.
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Statistics and Machine Learning Toolbox™ Release Notes
© COPYRIGHT 2005–2016 by The MathWorks, Inc.
The software described in this document is furnished under a license agreement. The software may be used
or copied only under the terms of the license agreement. No part of this manual may be photocopied or
reproduced in any form without prior written consent from The MathWorks, Inc.
FEDERAL ACQUISITION: This provision applies to all acquisitions of the Program and Documentation
by, for, or through the federal government of the United States. By accepting delivery of the Program
or Documentation, the government hereby agrees that this software or documentation qualifies as
commercial computer software or commercial computer software documentation as such terms are used
or defined in FAR 12.212, DFARS Part 227.72, and DFARS 252.227-7014. Accordingly, the terms and
conditions of this Agreement and only those rights specified in this Agreement, shall pertain to and
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iii
Contents
R2016a
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 . . . 1-2
Classification Learner: Train multiple models automatically,
visualize results by class labels, and perform logistic
regression classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-3
Performance: Perform clustering using kmeans, kmedoids,
and Gaussian mixture models faster when data has a large
number of clusters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-3
Probability Distributions: Fit kernel smoothing density to
multivariate data using the ksdensity and mvksdensity
functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-3
Stable Distributions: Model financial and other data that
requires heavy-tailed distributions . . . . . . . . . . . . . . . . . . . 1-4
Half-Normal Distributions: Model truncated data and create
half-normal probability plots . . . . . . . . . . . . . . . . . . . . . . . . 1-4
Linear Regression: CompactLinearModel object reduces
memory footprint of linear regression model . . . . . . . . . . 1-4
Robust covariance estimation for multivariate sample data
using robustcov . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-4
Squared Euclidean distance measure for pdist and pdist2
functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-5
iv Contents
Performance enhancements for nearest neighbor search
using kd-tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-5
GPU support for extreme value distribution functions and
kmeans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-5
Changes to default online update phase for kmeans
function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-5
Name change in ksdensity . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-6
Name change in paretotails . . . . . . . . . . . . . . . . . . . . . . . . . 1-6
Functionality Being Changed . . . . . . . . . . . . . . . . . . . . . . . . . 1-6
R2015b
Classification Learner: Train discriminant analysis to
classify data, train models using categorical predictors,
and perform dimensionality reduction using PCA . . . . . . 2-2
Nonparametric Regression: Fit models using support vector
regression (SVR) or Gaussian processes (Kriging) . . . . . . 2-5
Tables and Categorical Data for Machine Learning: Use
table and categorical predictors in classification and
nonparametric regression functions and in Classification
Learner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-6
Code Generation: Automatically generate C and C++ code for
kmeans and randsample functions (using MATLAB Coder)
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-6
GPU Acceleration: Speed up computation for over 65
functions including probability distributions, descriptive
statistics, and hypothesis testing (using Parallel
Computing Toolbox) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-7
Option to turn off clipping of Alpha coefficients in fitcsvm 2-7
v
Name changes in TreeBagger . . . . . . . . . . . . . . . . . . . . . . . . . . 2-7
R2015a
Classification app to train models and classify data using
supervised machine learning . . . . . . . . . . . . . . . . . . . . . . . . 3-2
Statistical tests for comparing accuracies of two
classification models using compareHoldout, testcholdout,
and testckfold functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-3
Speedup of kmedoids, fitcknn, and other functions
when using cosine, correlation, or spearman distance
calculations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-3
Performance enhancements for decision trees and
performance curves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-3
Additional option to control decision tree depth using
'MaxNumSplits' argument in fitctree, fitrtree, and
templateTree functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-4
Code generation for pca and probability distribution
functions (using MATLAB Coder) . . . . . . . . . . . . . . . . . . . . 3-4
Power and sample size for two-sample t-test using
sampsizepwr function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-4
Discard support vectors of SVM and ECOC models . . . . . . . 3-4
Minimum leaf size for boosted regression trees . . . . . . . . . . 3-5
Additional option to plot grouped histograms using the
scatterhist and gplotmatrix functions . . . . . . . . . . . . . . 3-5
Confidence interval computation for residuals using the
function regress . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-6
Functionality Being Changed . . . . . . . . . . . . . . . . . . . . . . . . . 3-6
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