### mlpack 3.2.1
###### 2019-10-01
  * Enforce CMake version check for ensmallen [#2032](https://github.com/mlpack/mlpack/issues/2032).

  * Fix CMake check for Armadillo version [#2029](https://github.com/mlpack/mlpack/issues/2029).

  * Better handling of when STB is not installed [#2033](https://github.com/mlpack/mlpack/issues/2033).

  * Fix Naive Bayes classifier computations in high dimensions [#2022](https://github.com/mlpack/mlpack/issues/2022).

### mlpack 3.2.0
###### 2019-09-25
  * Fix some potential infinity errors in Naive Bayes Classifier [#2022](https://github.com/mlpack/mlpack/issues/2022).

  * Fix occasionally-failing RADICAL test [#1924](https://github.com/mlpack/mlpack/issues/1924).

  * Fix gcc 9 OpenMP compilation issue [#1970](https://github.com/mlpack/mlpack/issues/1970).

  * Added support for loading and saving of images [#1903](https://github.com/mlpack/mlpack/issues/1903).

  * Add Multiple Pole Balancing Environment (#1901, #1951).

  * Added functionality for scaling of data [#1876](https://github.com/mlpack/mlpack/issues/1876); see the command-line
    binding `mlpack_preprocess_scale` or Python binding `preprocess_scale()`.

  * Add new parameter `maximum_depth` to decision tree and random forest
    bindings [#1916](https://github.com/mlpack/mlpack/issues/1916).

  * Fix prediction output of softmax regression when test set accuracy is
    calculated [#1922](https://github.com/mlpack/mlpack/issues/1922).

  * Pendulum environment now checks for termination. All RL environments now
    have an option to terminate after a set number of time steps (no limit
    by default) [#1941](https://github.com/mlpack/mlpack/issues/1941).

  * Add support for probabilistic KDE (kernel density estimation) error bounds
    when using the Gaussian kernel [#1934](https://github.com/mlpack/mlpack/issues/1934).

  * Fix negative distances for cover tree computation [#1979](https://github.com/mlpack/mlpack/issues/1979).

  * Fix cover tree building when all pairwise distances are 0 [#1986](https://github.com/mlpack/mlpack/issues/1986).

  * Improve KDE pruning by reclaiming not used error tolerance (#1954, #1984).

  * Optimizations for sparse matrix accesses in z-score normalization for CF
    [#1989](https://github.com/mlpack/mlpack/issues/1989).

  * Add `kmeans_max_iterations` option to GMM training binding `gmm_train_main`.

  * Bump minimum Armadillo version to 8.400.0 due to ensmallen dependency
    requirement [#2015](https://github.com/mlpack/mlpack/issues/2015).

### mlpack 3.1.1
###### 2019-05-26
  * Fix random forest bug for numerical-only data [#1887](https://github.com/mlpack/mlpack/issues/1887).

  * Significant speedups for random forest [#1887](https://github.com/mlpack/mlpack/issues/1887).

  * Random forest now has `minimum_gain_split` and `subspace_dim` parameters
    [#1887](https://github.com/mlpack/mlpack/issues/1887).

  * Decision tree parameter `print_training_error` deprecated in favor of
    `print_training_accuracy`.

  * `output` option changed to `predictions` for adaboost and perceptron
    binding. Old options are now deprecated and will be preserved until mlpack
    4.0.0 [#1882](https://github.com/mlpack/mlpack/issues/1882).

  * Concatenated ReLU layer [#1843](https://github.com/mlpack/mlpack/issues/1843).

  * Accelerate NormalizeLabels function using hashing instead of linear search
    (see `src/mlpack/core/data/normalize_labels_impl.hpp`) [#1780](https://github.com/mlpack/mlpack/issues/1780).

  * Add `ConfusionMatrix()` function for checking performance of classifiers
    [#1798](https://github.com/mlpack/mlpack/issues/1798).

  * Install ensmallen headers when it is downloaded during build [#1900](https://github.com/mlpack/mlpack/issues/1900).

### mlpack 3.1.0
###### 2019-04-25
  * Add DiagonalGaussianDistribution and DiagonalGMM classes to speed up the
    diagonal covariance computation and deprecate DiagonalConstraint [#1666](https://github.com/mlpack/mlpack/issues/1666).

  * Add kernel density estimation (KDE) implementation with bindings to other
    languages [#1301](https://github.com/mlpack/mlpack/issues/1301).

  * Where relevant, all models with a `Train()` method now return a `double`
    value representing the goodness of fit (i.e. final objective value, error,
    etc.) [#1678](https://github.com/mlpack/mlpack/issues/1678).

  * Add implementation for linear support vector machine (see
    `src/mlpack/methods/linear_svm`).

  * Change DBSCAN to use PointSelectionPolicy and add OrderedPointSelection [#1625](https://github.com/mlpack/mlpack/issues/1625).

  * Residual block support [#1594](https://github.com/mlpack/mlpack/issues/1594).

  * Bidirectional RNN [#1626](https://github.com/mlpack/mlpack/issues/1626).

  * Dice loss layer (#1674, #1714) and hard sigmoid layer [#1776](https://github.com/mlpack/mlpack/issues/1776).

  * `output` option changed to `predictions` and `output_probabilities` to
    `probabilities` for Naive Bayes binding (`mlpack_nbc`/`nbc()`).  Old options
    are now deprecated and will be preserved until mlpack 4.0.0 [#1616](https://github.com/mlpack/mlpack/issues/1616).

  * Add support for Diagonal GMMs to HMM code (#1658, #1666).  This can provide
    large speedup when a diagonal GMM is acceptable as an emission probability
    distribution.

  * Python binding improvements: check parameter type [#1717](https://github.com/mlpack/mlpack/issues/1717), avoid copying
    Pandas dataframes [#1711](https://github.com/mlpack/mlpack/issues/1711), handle Pandas Series objects [#1700](https://github.com/mlpack/mlpack/issues/1700).

### mlpack 3.0.4
###### 2018-11-13
  * Bump minimum CMake version to 3.3.2.

  * CMake fixes for Ninja generator by Marc Espie.

### mlpack 3.0.3
###### 2018-07-27
  * Fix Visual Studio compilation issue [#1443](https://github.com/mlpack/mlpack/issues/1443).

  * Allow running local_coordinate_coding binding with no initial_dictionary
    parameter when input_model is not specified [#1457](https://github.com/mlpack/mlpack/issues/1457).

  * Make use of OpenMP optional via the CMake 'USE_OPENMP' configuration
    variable [#1474](https://github.com/mlpack/mlpack/issues/1474).

  * Accelerate FNN training by 20-30% by avoiding redundant calculations
    [#1467](https://github.com/mlpack/mlpack/issues/1467).

  * Fix math::RandomSeed() usage in tests (#1462, #1440).

  * Generate better Python setup.py with documentation [#1460](https://github.com/mlpack/mlpack/issues/1460).

### mlpack 3.0.2
###### 2018-06-08
  * Documentation generation fixes for Python bindings [#1421](https://github.com/mlpack/mlpack/issues/1421).

  * Fix build error for man pages if command-line bindings are not being built
    [#1424](https://github.com/mlpack/mlpack/issues/1424).

  * Add 'shuffle' parameter and Shuffle() method to KFoldCV [#1412](https://github.com/mlpack/mlpack/issues/1412).  This will
    shuffle the data when the object is constructed, or when Shuffle() is
    called.

  * Added neural network layers: AtrousConvolution [#1390](https://github.com/mlpack/mlpack/issues/1390), Embedding [#1401](https://github.com/mlpack/mlpack/issues/1401),
    and LayerNorm (layer normalization) [#1389](https://github.com/mlpack/mlpack/issues/1389).

  * Add Pendulum environment for reinforcement learning [#1388](https://github.com/mlpack/mlpack/issues/1388) and update
    Mountain Car environment [#1394](https://github.com/mlpack/mlpack/issues/1394).

### mlpack 3.0.1
###### 2018-05-10
  * Fix intermittently failing tests [#1387](https://github.com/mlpack/mlpack/issues/1387).

  * Add big-batch SGD (BBSGD) optimizer in
    src/mlpack/core/optimizers/bigbatch_sgd/ [#1131](https://github.com/mlpack/mlpack/issues/1131).

  * Fix simple compiler warnings (#1380, #1373).

  * Simplify NeighborSearch constructor and Train() overloads [#1378](https://github.com/mlpack/mlpack/issues/1378).

  * Add warning for OpenMP setting differences (#1358/#1382).  When mlpack is
    compiled with OpenMP but another application is not (or vice versa), a
    compilation warning will now be issued.

  * Restructured loss functions in src/mlpack/methods/ann/ [#1365](https://github.com/mlpack/mlpack/issues/1365).

  * Add environments for reinforcement learning tests (#1368, #1370, #1329).

  * Allow single outputs for multiple timestep inputs for recurrent neural
    networks [#1348](https://github.com/mlpack/mlpack/issues/1348).

  * Add He and LeCun normal initializations for neural networks [#1342](https://github.com/mlpack/mlpack/issues/1342).
    Neural networks: add He and LeCun normal initializations [#1342](https://github.com/mlpack/mlpack/issues/1342), add FReLU
    and SELU activation functions (#1346, #1341), add alpha-dropout [#1349](https://github.com/mlpack/mlpack/issues/1349).

### mlpack 3.0.0
###### 2018-03-30
  * Speed and memory improvements for DBSCAN.  --single_mode can now be used for
    situations where previously RAM usage was too high.

  * Bump minimum required version of Armadillo to 6.500.0.

  * Add automatically generated Python bindings.  These have the same interface
    as the command-line programs.

  * Add deep learning infrastructure in src/mlpack/methods/ann/.

  * Add reinforcement learning infrastructure in
    src/mlpack/methods/reinforcement_learning/.

  * Add optimizers: AdaGrad, CMAES, CNE, FrankeWolfe, GradientDescent,
    GridSearch, IQN, Katyusha, LineSearch, ParallelSGD, SARAH, SCD, SGDR,
    SMORMS3, SPALeRA, SVRG.

  * Add hyperparameter tuning infrastructure and cross-validation infrastructure
    in src/mlpack/core/cv/ and src/mlpack/core/hpt/.

  * Fix bug in mean shift.

  * Add random forests (see src/mlpack/methods/random_forest).

  * Numerous other bugfixes and testing improvements.

  * Add randomized Krylov SVD and Block Krylov SVD.

### mlpack 2.2.5
###### 2017-08-25
  * Compilation fix for some systems [#1082](https://github.com/mlpack/mlpack/issues/1082).

  * Fix PARAM_INT_OUT() [#1100](https://github.com/mlpack/mlpack/issues/1100).

### mlpack 2.2.4
###### 2017-07-18
  * Speed and memory improvements for DBSCAN. --single_mode can now be used for
    situations where previously RAM usage was too high.

  * Fix bug in CF causing incorrect recommendations.

### mlpack 2.2.3
###### 2017-05-24
  * Bug fix for --predictions_file in mlpack_decision_tree program.

### mlpack 2.2.2
###### 2017-05-04
  * Install backwards-compatibility mlpack_allknn and mlpack_allkfn programs;
    note they are deprecated and will be removed in mlpack 3.0.0 [#992](https://github.com/mlpack/mlpack/issues/992).

  * Fix RStarTree bug that surfaced on OS X only [#964](https://github.com/mlpack/mlpack/issues/964).

  * Small fixes for MiniBatchSGD and SGD and tests.

### mlpack 2.2.1
###### 2017-04-13
  * Compilation fix for mlpack_nca and mlpack_test on older Armadillo versions
    [#984](https://github.com/mlpack/mlpack/issues/984).

### mlpack 2.2.0
###### 2017-03-21
  * Bugfix for mlpack_knn program [#816](https://github.com/mlpack/mlpack/issues/816).

  * Add decision tree implementation in methods/decision_tree/.  This is very
    similar to a C4.5 tree learner.

  * Add DBSCAN implementation in methods/dbscan/.

  * Add support for multidimensional discrete distributions (#810, #830).

  * Better output for Log::Debug/Log::Info/Log::Warn/Log::Fatal for Armadillo
    objects (#895, #928).

  * Refactor categorical CSV loading with boost::spirit for faster loading
    [#681](https://github.com/mlpack/mlpack/issues/681).

### mlpack 2.1.1
###### 2016-12-22
  * HMMs now use random initialization; this should fix some convergence issues
    [#828](https://github.com/mlpack/mlpack/issues/828).

  * HMMs now initialize emissions according to the distribution of observations
    [#833](https://github.com/mlpack/mlpack/issues/833).

  * Minor fix for formatted output [#814](https://github.com/mlpack/mlpack/issues/814).

  * Fix DecisionStump to properly work with any input type.

### mlpack 2.1.0
###### 2016-10-31
  * Fixed CoverTree to properly handle single-point datasets.

  * Fixed a bug in CosineTree (and thus QUIC-SVD) that caused split failures for
    some datasets [#717](https://github.com/mlpack/mlpack/issues/717).

  * Added mlpack_preprocess_describe program, which can be used to print
    statistics on a given dataset [#742](https://github.com/mlpack/mlpack/issues/742).

  * Fix prioritized recursion for k-furthest-neighbor search (mlpack_kfn and the
    KFN class), leading to orders-of-magnitude speedups in some cases.

  * Bump minimum required version of Armadillo to 4.200.0.

  * Added simple Gradient Descent optimizer, found in
    src/mlpack/core/optimizers/gradient_descent/ [#792](https://github.com/mlpack/mlpack/issues/792).

  * Added approximate furthest neighbor search algorithms QDAFN and
    DrusillaSelect in src/mlpack/methods/approx_kfn/, with command-line program
    mlpack_approx_kfn.

### mlpack 2.0.3
###### 2016-07-21
  * Added multiprobe LSH [#691](https://github.com/mlpack/mlpack/issues/691).  The parameter 'T' to LSHSearch::Search() can
    now be used to control the number of extra bins that are probed, as can the
    -T (--num_probes) option to mlpack_lsh.

  * Added the Hilbert R tree to src/mlpack/core/tree/rectangle_tree/ [#664](https://github.com/mlpack/mlpack/issues/664).  It
    can be used as the typedef HilbertRTree, and it is now an option in the
    mlpack_knn, mlpack_kfn, mlpack_range_search, and mlpack_krann command-line
    programs.

  * Added the mlpack_preprocess_split and mlpack_preprocess_binarize programs,
    which can be used for preprocessing code (#650, #666).

  * Added OpenMP support to LSHSearch and mlpack_lsh [#700](https://github.com/mlpack/mlpack/issues/700).

### mlpack 2.0.2
###### 2016-06-20
  * Added the function LSHSearch::Projections(), which returns an arma::cube
    with each projection table in a slice [#663](https://github.com/mlpack/mlpack/issues/663).  Instead of Projection(i), you
    should now use Projections().slice(i).

  * A new constructor has been added to LSHSearch that creates objects using
    projection tables provided in an arma::cube [#663](https://github.com/mlpack/mlpack/issues/663).

  * Handle zero-variance dimensions in DET [#515](https://github.com/mlpack/mlpack/issues/515).

  * Add MiniBatchSGD optimizer (src/mlpack/core/optimizers/minibatch_sgd/) and
    allow its use in mlpack_logistic_regression and mlpack_nca programs.

  * Add better backtrace support from Grzegorz Krajewski for Log::Fatal messages
    when compiled with debugging and profiling symbols.  This requires libbfd
    and libdl to be present during compilation.

  * CosineTree test fix from Mikhail Lozhnikov [#358](https://github.com/mlpack/mlpack/issues/358).

  * Fixed HMM initial state estimation [#600](https://github.com/mlpack/mlpack/issues/600).

  * Changed versioning macros __MLPACK_VERSION_MAJOR, __MLPACK_VERSION_MINOR,
    and __MLPACK_VERSION_PATCH to MLPACK_VERSION_MAJOR, MLPACK_VERSION_MINOR,
    and MLPACK_VERSION_PATCH.  The old names will remain in place until
    mlpack 3.0.0.

  * Renamed mlpack_allknn, mlpack_allkfn, and mlpack_allkrann to mlpack_knn,
    mlpack_kfn, and mlpack_krann.  The mlpack_allknn, mlpack_allkfn, and
    mlpack_allkrann programs will remain as copies until mlpack 3.0.0.

  * Add --random_initialization option to mlpack_hmm_train, for use when no
    labels are provided.

  * Add --kill_empty_clusters option to mlpack_kmeans and KillEmptyClusters
    policy for the KMeans class (#595, #596).

### mlpack 2.0.1
###### 2016-02-04
  * Fix CMake to properly detect when MKL is being used with Armadillo.

  * Minor parameter handling fixes to mlpack_logistic_regression (#504, #505).

  * Properly install arma_config.hpp.

  * Memory handling fixes for Hoeffding tree code.

  * Add functions that allow changing training-time parameters to HoeffdingTree
    class.

  * Fix infinite loop in sparse coding test.

  * Documentation spelling fixes [#501](https://github.com/mlpack/mlpack/issues/501).

  * Properly handle covariances for Gaussians with large condition number
    [#496](https://github.com/mlpack/mlpack/issues/496), preventing GMMs from filling with NaNs during training (and also
    HMMs that use GMMs).

  * CMake fixes for finding LAPACK and BLAS as Armadillo dependencies when ATLAS
    is used.

  * CMake fix for projects using mlpack's CMake configuration from elsewhere
    [#512](https://github.com/mlpack/mlpack/issues/512).

### mlpack 2.0.0
###### 2015-12-24
  * Removed overclustering support from k-means because it is not well-tested,
    may be buggy, and is (I think) unused.  If this was support you were using,
    open a bug or get in touch with us; it would not be hard for us to
    reimplement it.

  * Refactored KMeans to allow different types of Lloyd iterations.

  * Added implementations of k-means: Elkan's algorithm, Hamerly's algorithm,
    Pelleg-Moore's algorithm, and the DTNN (dual-tree nearest neighbor)
    algorithm.

  * Significant acceleration of LRSDP via the use of accu(a % b) instead of
    trace(a * b).

  * Added MatrixCompletion class (matrix_completion), which performs nuclear
    norm minimization to fill unknown values of an input matrix.

  * No more dependence on Boost.Random; now we use C++11 STL random support.

  * Add softmax regression, contributed by Siddharth Agrawal and QiaoAn Chen.

  * Changed NeighborSearch, RangeSearch, FastMKS, LSH, and RASearch API; these
    classes now take the query sets in the Search() method, instead of in the
    constructor.

  * Use OpenMP, if available.  For now OpenMP support is only available in the
    DET training code.

  * Add support for predicting new test point values to LARS and the
    command-line 'lars' program.

  * Add serialization support for Perceptron and LogisticRegression.

  * Refactor SoftmaxRegression to predict into an arma::Row<size_t> object, and
    add a softmax_regression program.

  * Refactor LSH to allow loading and saving of models.

  * ToString() is removed entirely [#487](https://github.com/mlpack/mlpack/issues/487).

  * Add --input_model_file and --output_model_file options to appropriate
    machine learning algorithms.

  * Rename all executables to start with an "mlpack" prefix [#229](https://github.com/mlpack/mlpack/issues/229).

  * Add HoeffdingTree and mlpack_hoeffding_tree, an implementation of the
    streaming decision tree methodology from Domingos and Hulten in 2000.

### mlpack 1.0.12
###### 2015-01-07
  * Switch to 3-clause BSD license (from LGPL).

### mlpack 1.0.11
###### 2014-12-11
  * Proper handling of dimension calculation in PCA.

  * Load parameter vectors properly for LinearRegression models.

  * Linker fixes for AugLagrangian specializations under Visual Studio.

  * Add support for observation weights to LinearRegression.

  * MahalanobisDistance<> now takes the root of the distance by default and
    therefore satisfies the triangle inequality (TakeRoot now defaults to true).

  * Better handling of optional Armadillo HDF5 dependency.

  * Fixes for numerous intermittent test failures.

  * math::RandomSeed() now sets the random seed for recent (>=3.930) Armadillo
    versions.

  * Handle Newton method convergence better for
    SparseCoding::OptimizeDictionary() and make maximum iterations a parameter.

  * Known bug: CosineTree construction may fail in some cases on i386 systems
    [#358](https://github.com/mlpack/mlpack/issues/358).

### mlpack 1.0.10
###### 2014-08-29
  * Bugfix for NeighborSearch regression which caused very slow allknn/allkfn.
    Speeds are now restored to approximately 1.0.8 speeds, with significant
    improvement for the cover tree [#347](https://github.com/mlpack/mlpack/issues/347).

  * Detect dependencies correctly when ARMA_USE_WRAPPER is not being defined
    (i.e., libarmadillo.so does not exist).

  * Bugfix for compilation under Visual Studio [#348](https://github.com/mlpack/mlpack/issues/348).

### mlpack 1.0.9
###### 2014-07-28
  * GMM initialization is now safer and provides a working GMM when constructed
    with only the dimensionality and number of Gaussians [#301](https://github.com/mlpack/mlpack/issues/301).

  * Check for division by 0 in Forward-Backward Algorithm in HMMs [#301](https://github.com/mlpack/mlpack/issues/301).

  * Fix MaxVarianceNewCluster (used when re-initializing clusters for k-means)
    [#301](https://github.com/mlpack/mlpack/issues/301).

  * Fixed implementation of Viterbi algorithm in HMM::Predict() [#303](https://github.com/mlpack/mlpack/issues/303).

  * Significant speedups for dual-tree algorithms using the cover tree (#235,
    #314) including a faster implementation of FastMKS.

  * Fix for LRSDP optimizer so that it compiles and can be used [#312](https://github.com/mlpack/mlpack/issues/312).

  * CF (collaborative filtering) now expects users and items to be zero-indexed,
    not one-indexed [#311](https://github.com/mlpack/mlpack/issues/311).

  * CF::GetRecommendations() API change: now requires the number of
    recommendations as the first parameter.  The number of users in the local
    neighborhood should be specified with CF::NumUsersForSimilarity().

  * Removed incorrect PeriodicHRectBound [#58](https://github.com/mlpack/mlpack/issues/58).

  * Refactor LRSDP into LRSDP class and standalone function to be optimized
    [#305](https://github.com/mlpack/mlpack/issues/305).

  * Fix for centering in kernel PCA [#337](https://github.com/mlpack/mlpack/issues/337).

  * Added simulated annealing (SA) optimizer, contributed by Zhihao Lou.

  * HMMs now support initial state probabilities; these can be set in the
    constructor, trained, or set manually with HMM::Initial() [#302](https://github.com/mlpack/mlpack/issues/302).

  * Added Nyström method for kernel matrix approximation by Marcus Edel.

  * Kernel PCA now supports using Nyström method for approximation.

  * Ball trees now work with dual-tree algorithms, via the BallBound<> bound
    structure [#307](https://github.com/mlpack/mlpack/issues/307); fixed by Yash Vadalia.

  * The NMF class is now AMF<>, and supports far more types of factorizations,
    by Sumedh Ghaisas.

  * A QUIC-SVD implementation has returned, written by Siddharth Agrawal and
    based on older code from Mudit Gupta.

  * Added perceptron and decision stump by Udit Saxena (these are weak learners
    for an eventual AdaBoost class).

  * Sparse autoencoder added by Siddharth Agrawal.

### mlpack 1.0.8
###### 2014-01-06
  * Memory leak in NeighborSearch index-mapping code fixed [#298](https://github.com/mlpack/mlpack/issues/298).

  * GMMs can be trained using the existing model as a starting point by
    specifying an additional boolean parameter to GMM::Estimate() [#296](https://github.com/mlpack/mlpack/issues/296).

  * Logistic regression implementation added in methods/logistic_regression (see
    also #293).

  * L-BFGS optimizer now returns its function via Function().

  * Version information is now obtainable via mlpack::util::GetVersion() or the
    __MLPACK_VERSION_MAJOR, __MLPACK_VERSION_MINOR, and  __MLPACK_VERSION_PATCH
    macros [#297](https://github.com/mlpack/mlpack/issues/297).

  * Fix typos in allkfn and allkrann output.

### mlpack 1.0.7
###### 2013-10-04
  * Cover tree support for range search (range_search), rank-approximate nearest
    neighbors (allkrann), minimum spanning tree calculation (emst), and FastMKS
    (fastmks).

  * Dual-tree FastMKS implementation added and tested.

  * Added collaborative filtering package (cf) that can provide recommendations
    when given users and items.

  * Fix for correctness of Kernel PCA (kernel_pca) [#270](https://github.com/mlpack/mlpack/issues/270).

  * Speedups for PCA and Kernel PCA [#198](https://github.com/mlpack/mlpack/issues/198).

  * Fix for correctness of Neighborhood Components Analysis (NCA) [#279](https://github.com/mlpack/mlpack/issues/279).

  * Minor speedups for dual-tree algorithms.

  * Fix for Naive Bayes Classifier (nbc) [#269](https://github.com/mlpack/mlpack/issues/269).

  * Added a ridge regression option to LinearRegression (linear_regression)
    [#286](https://github.com/mlpack/mlpack/issues/286).

  * Gaussian Mixture Models (gmm::GMM<>) now support arbitrary covariance matrix
    constraints [#283](https://github.com/mlpack/mlpack/issues/283).

  * MVU (mvu) removed because it is known to not work [#183](https://github.com/mlpack/mlpack/issues/183).

  * Minor updates and fixes for kernels (in mlpack::kernel).

### mlpack 1.0.6
###### 2013-06-13
  * Minor bugfix so that FastMKS gets built.

### mlpack 1.0.5
###### 2013-05-01
  * Speedups of cover tree traversers [#235](https://github.com/mlpack/mlpack/issues/235).

  * Addition of rank-approximate nearest neighbors (RANN), found in
    src/mlpack/methods/rann/.

  * Addition of fast exact max-kernel search (FastMKS), found in
    src/mlpack/methods/fastmks/.

  * Fix for EM covariance estimation; this should improve GMM training time.

  * More parameters for GMM estimation.

  * Force GMM and GaussianDistribution covariance matrices to be positive
    definite, so that training converges much more often.

  * Add parameter for the tolerance of the Baum-Welch algorithm for HMM
    training.

  * Fix for compilation with clang compiler.

  * Fix for k-furthest-neighbor-search.

### mlpack 1.0.4
###### 2013-02-08
  * Force minimum Armadillo version to 2.4.2.

  * Better output of class types to streams; a class with a ToString() method
    implemented can be sent to a stream with operator<<.

  * Change return type of GMM::Estimate() to double [#257](https://github.com/mlpack/mlpack/issues/257).

  * Style fixes for k-means and RADICAL.

  * Handle size_t support correctly with Armadillo 3.6.2 [#258](https://github.com/mlpack/mlpack/issues/258).

  * Add locality-sensitive hashing (LSH), found in src/mlpack/methods/lsh/.

  * Better tests for SGD (stochastic gradient descent) and NCA (neighborhood
    components analysis).

### mlpack 1.0.3
###### 2012-09-16

  * Remove internal sparse matrix support because Armadillo 3.4.0 now includes
    it.  When using Armadillo versions older than 3.4.0, sparse matrix support
    is not available.

  * NCA (neighborhood components analysis) now support an arbitrary optimizer
    [#245](https://github.com/mlpack/mlpack/issues/245), including stochastic gradient descent [#249](https://github.com/mlpack/mlpack/issues/249).

### mlpack 1.0.2
###### 2012-08-15
  * Added density estimation trees, found in src/mlpack/methods/det/.

  * Added non-negative matrix factorization, found in src/mlpack/methods/nmf/.

  * Added experimental cover tree implementation, found in
    src/mlpack/core/tree/cover_tree/ [#157](https://github.com/mlpack/mlpack/issues/157).

  * Better reporting of boost::program_options errors [#225](https://github.com/mlpack/mlpack/issues/225).

  * Fix for timers on Windows (#212, #211).

  * Fix for allknn and allkfn output [#204](https://github.com/mlpack/mlpack/issues/204).

  * Sparse coding dictionary initialization is now a template parameter [#220](https://github.com/mlpack/mlpack/issues/220).

### mlpack 1.0.1
###### 2012-03-03
  * Added kernel principal components analysis (kernel PCA), found in
    src/mlpack/methods/kernel_pca/ [#74](https://github.com/mlpack/mlpack/issues/74).

  * Fix for Lovasz-Theta AugLagrangian tests [#182](https://github.com/mlpack/mlpack/issues/182).

  * Fixes for allknn output (#185, #186).

  * Added range search executable [#192](https://github.com/mlpack/mlpack/issues/192).

  * Adapted citations in documentation to BibTeX; no citations in -h output
    [#195](https://github.com/mlpack/mlpack/issues/195).

  * Stop use of 'const char*' and prefer 'std::string' [#176](https://github.com/mlpack/mlpack/issues/176).

  * Support seeds for random numbers [#177](https://github.com/mlpack/mlpack/issues/177).

### mlpack 1.0.0
###### 2011-12-17
  * Initial release.  See any resolved tickets numbered less than #196 or
    execute this query:
    http://www.mlpack.org/trac/query?status=closed&milestone=mlpack+1.0.0