<commit_before>### mlpack 1.1.0
###### ????-??-??
  * 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 <NAME> and <NAME>.

  * Changed NeighborSearch, RangeSearch, FastMKS, 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.

### 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 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).

### 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).<commit_msg>Note the changes that have been made.<commit_after>### mlpack 1.1.0
###### ????-??-??
  * 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 <NAME> and <NAME>.

  * Changed NeighborSearch, RangeSearch, FastMKS, 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.

### 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 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).

### 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).
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