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Logo pyGPs 1.2

by mn - July 17, 2014, 10:28:55 CET [ Project Homepage BibTeX Download ] 1826 views, 440 downloads, 2 subscriptions

About: pyGPs is a Python package for Gaussian process (GP) regression and classification for machine learning.

Changes:

Changelog pyGPs v1.2

June 30th 2014

structural updates:

  • input target now can either be in 2-d array with size (n,1) or in 1-d array with size (n,)
  • setup.py updated
  • "import pyGPs" instead of "from pyGPs.Core import gp"
  • rename ".train()" to ".optimize()"
  • rename "Graph-stuff" to "graphExtension"
  • rename kernelOnGraph to "nodeKernels" and graphKernel to "graphKernels"
  • redundancy removed for model.setData(x,y)
  • rewrite "mean.proceed()" to "getMean()" and "getDerMatrix()"
  • rewrite "cov.proceed()" to "getCovMatrix()" and "getDerMatrix()"
  • rename cov.LIN to cov.Linear (to be consistent with mean.Linear)
  • rename module "valid" to "validation"
  • add graph dataset Mutag in python file. (.npz and .mat)
  • add graphUtil.nomalizeKernel()
  • fix number of iteration problem in graphKernels "PropagationKernel"
  • add unit testing for covariance, mean functions

bug fixes:

  • derivatives for cov.LINard
  • derivative of the scalar for cov.covScale
  • demo_GPR_FITC.py missing pyGPs.mean

July 8th 2014

structural updates:

  • add hyperparameter(signal variance s2) for linear covariance
  • add unit testing for inference,likelihood functions as well as models
  • NOT show(print) "maximum number of sweep warning in inference EP" any more
  • documentation updated

bug fixes:

  • typos in lik.Laplace
  • derivative in lik.Laplace

July 14th 2014

documentation updates:

  • online docs updated
  • API file updated

structural updates:

  • made private for methods that users don't need to call

About: This code is developed for incorporating a class of linear priors into the regression model.

Changes:

Initial Announcement on mloss.org.


About: Ran He, Wei-Shi Zheng,Tieniu Tan, and Zhenan Sun. Half-quadratic based Iterative Minimization for Robust Sparse Representation. Submitted to IEEE Trans. on Pattern Analysis and Machine Intelligence.

Changes:

Initial Announcement on mloss.org.


Logo Multilinear Principal Component Analysis 1.2 1.2

by openpr_nlpr - April 16, 2012, 09:04:08 CET [ Project Homepage BibTeX Download ] 1727 views, 524 downloads, 1 subscription

About: This archive contains a Matlab implementation of the Multilinear Principal Component Analysis (MPCA) algorithm and MPCA+LDA, as described in the paper Haiping Lu, K.N. Plataniotis, and A.N. Venetsanopoulos, "MPCA: Multilinear Principal Component Analysis of Tensor Objects", IEEE Transactions on Neural Networks, Vol. 19, No. 1, Page: 18-39, January 2008.

Changes:

Initial Announcement on mloss.org.


Logo PILCO policy search framework 0.9

by marc - September 27, 2013, 12:45:12 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1720 views, 344 downloads, 1 subscription

About: Data-efficient policy search framework using probabilistic Gaussian process models

Changes:

Initial Announcement on mloss.org.


Logo Partition Comparison 1.0

by andres - April 21, 2012, 03:26:47 CET [ Project Homepage BibTeX Download ] 1718 views, 428 downloads, 1 subscription

About: Fast C++ implementation of the variation of information (Meila 2003) and Rand index (Rand 1971) with MATLAB mex files

Changes:

Initial Announcement on mloss.org.


Logo SFPD 1

by zenog - September 21, 2011, 14:26:45 CET [ Project Homepage BibTeX Download ] 1703 views, 406 downloads, 1 subscription

About: Survival forests: Random Forests variant for survival analysis. Original implementation by Leo Breiman.

Changes:

Initial Announcement on mloss.org.


Logo GP RTSS 1.0

by marc - March 21, 2012, 08:43:52 CET [ BibTeX BibTeX for corresponding Paper Download ] 1700 views, 549 downloads, 1 subscription

About: Gaussian process RTS smoothing (forward-backward smoothing) based on moment matching.

Changes:

Initial Announcement on mloss.org.


Logo RRforest 2002-03-13

by zenog - September 21, 2011, 14:23:44 CET [ Project Homepage BibTeX Download ] 1674 views, 435 downloads, 1 subscription

About: Regression forests, Random Forests for regression. Original implementation by Leo Breiman.

Changes:

Initial Announcement on mloss.org.


Logo MROGH 1.0

by openpr_nlpr - October 16, 2012, 04:41:51 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1663 views, 347 downloads, 1 subscription

About: An implementation of MROGH descriptor. For more information, please refer to: “Bin Fan, Fuchao Wu and Zhanyi Hu, Aggregating Gradient Distributions into Intensity Orders: A Novel Local Image Descriptor, CVPR 2011, pp.2377-2384.” The most up-to-date information can be found at : http://vision.ia.ac.cn/Students/bfan/index.htm

Changes:

Initial Announcement on mloss.org.


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