All entries.
Showing Items 1-10 of 536 on page 1 of 54: 1 2 3 4 5 6 Next Last

Logo JMLR GPstuff 4.5

by avehtari - July 22, 2014, 14:03:11 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 10868 views, 2930 downloads, 2 subscriptions

Rating Whole StarWhole StarWhole StarWhole StarWhole Star
(based on 1 vote)

About: The GPstuff toolbox is a versatile collection of Gaussian process models and computational tools required for inference. The tools include, among others, various inference methods, sparse approximations and model assessment methods.

Changes:

2014-07-22 Version 4.5

New features

  • Input dependent noise and signal variance.

    • Tolvanen, V., Jylänki, P. and Vehtari, A. (2014). Expectation Propagation for Nonstationary Heteroscedastic Gaussian Process Regression. In Proceedings of IEEE International Workshop on Machine Learning for Signal Processing, accepted for publication. Preprint http://arxiv.org/abs/1404.5443
  • Sparse stochastic variational inference model.

    • Hensman, J., Fusi, N. and Lawrence, N. D. (2013). Gaussian processes for big data. arXiv preprint http://arxiv.org/abs/1309.6835.
  • Option 'autoscale' in the gp_rnd.m to get split normal approximated samples from the posterior predictive distribution of the latent variable.

    • Geweke, J. (1989). Bayesian Inference in Econometric Models Using Monte Carlo Integration. Econometrica, 57(6):1317-1339.

    • Villani, M. and Larsson, R. (2006). The Multivariate Split Normal Distribution and Asymmetric Principal Components Analysis. Communications in Statistics - Theory and Methods, 35(6):1123-1140.

Improvements

  • New unit test environment using the Matlab built-in test framework (the old Xunit package is still also supported).
  • Precomputed demo results (including the figures) are now available in the folder tests/realValues.
  • New demos demonstrating new features etc.
    • demo_epinf, demonstrating the input dependent noise and signal variance model
    • demo_svi_regression, demo_svi_classification
    • demo_modelcomparison2, demo_survival_comparison

Several minor bugfixes


Logo FEAST 1.1.1

by apocock - June 30, 2014, 01:30:23 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 13784 views, 3277 downloads, 1 subscription

Rating Whole StarWhole StarWhole StarWhole StarWhole Star
(based on 1 vote)

About: FEAST provides implementations of common mutual information based filter feature selection algorithms (mim, mifs, mrmr, cmim, icap, jmi, disr, fcbf, etc), and an implementation of RELIEF. Written for C/C++ & Matlab.

Changes:
  • Bug fixes to memory management.
  • Compatibility changes for PyFeast python wrapper (note the C library now returns feature indices starting from 0, the Matlab wrapper still returns indices starting from 1).
  • Added C version of MIM.
  • Updated internal version of MIToolbox.

Logo JMLR GPML Gaussian Processes for Machine Learning Toolbox 3.4

by hn - November 11, 2013, 14:46:52 CET [ Project Homepage BibTeX Download ] 17442 views, 4219 downloads, 3 subscriptions

Rating Whole StarWhole StarWhole StarWhole StarWhole Star
(based on 2 votes)

About: The GPML toolbox is a flexible and generic Octave 3.2.x and Matlab 7.x implementation of inference and prediction in Gaussian Process (GP) models.

Changes:
  • derivatives w.r.t. inducing points xu in infFITC, infFITC_Laplace, infFITC_EP so that one can treat the inducing points either as fixed given quantities or as additional hyperparameters
  • new GLM likelihood likExp for inter-arrival time modeling
  • new GLM likelihood likWeibull for extremal value regression
  • new GLM likelihood likGumbel for extremal value regression
  • new mean function meanPoly depending polynomially on the data
  • infExact can deal safely with the zero noise variance limit
  • support of GP warping through the new likelihood function likGaussWarp

Logo JMLR libDAI 0.3.1

by jorism - September 17, 2012, 14:17:03 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 32712 views, 6123 downloads, 2 subscriptions

Rating Whole StarWhole StarWhole StarWhole StarWhole Star
(based on 1 vote)

About: libDAI provides free & open source implementations of various (approximate) inference methods for graphical models with discrete variables, including Bayesian networks and Markov Random Fields.

Changes:

Release 0.3.1 fixes various bugs. The issues on 64-bit Windows platforms have been fixed and libDAI now offers full 64-bit support on all supported platforms (Linux, Mac OSX, Windows).


Logo TMBP 1.0

by zengjia - April 5, 2012, 06:42:26 CET [ BibTeX BibTeX for corresponding Paper Download ] 3613 views, 1809 downloads, 2 subscriptions

Rating Whole StarWhole StarWhole StarWhole StarWhole Star
(based on 1 vote)

About: Message passing for topic modeling

Changes:
  1. improve "readme.pdf".
  2. correct some compilation errors.

Logo LSTM for biological sequence analysis 1.0

by mhex - July 28, 2010, 16:32:29 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5149 views, 1199 downloads, 1 subscription

Rating Whole StarWhole StarWhole StarWhole StarWhole Star
(based on 1 vote)

About: Implementation of LSTM for biological sequence analysis (classification, regression, motif discovery, remote homology detection). Additionally a LSTM as logistic regression with spectrum kernel is included.

Changes:

Spectrum LSTM package included


Logo HSSVM 1.0.1

by xjbean - June 8, 2010, 16:16:05 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 8671 views, 1720 downloads, 1 subscription

Rating Whole StarWhole StarWhole StarWhole StarWhole Star
(based on 1 vote)

About: HSSVM is a software for solving multi-class problem using Hyper-sphere Support Vector Machines model, implemented by Java.

Changes:
  1. From this version, the version number is normalized to hssvm1.0.1;
  2. In this version, we delete the features about running parameter searching and run-all from Ant script, that is, commands "ant search-param" and "ant run-all" which exist in previous version are no longer available, and they are replaced with commands "svm search conf" and "svm runall conf", both of them are used on Linux(or all other POSIX systems).If you want to use this program on Windows, the cygwin is required to be installed.

Logo SimpleMKL 0.5

by arakotom - June 11, 2008, 00:56:47 CET [ Project Homepage BibTeX Download ] 8357 views, 2128 downloads, 5 subscriptions

Rating Whole StarWhole StarWhole StarWhole StarWhole Star
(based on 2 votes)

About: Matlab Multiple Kernel Learning toolbox. Features : MKL for SVM Classification, Regression and MultiClass. It needs SVM-KM Toolbox

Changes:

Initial Announcement on mloss.org.


Logo RapidMiner 4.0

by ingomierswa - November 16, 2007, 02:31:48 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 15118 views, 2571 downloads, 0 comments, 0 subscriptions

Rating Whole StarWhole StarWhole StarWhole StarWhole Star
(based on 5 votes)

About: RapidMiner (formerly YALE) is one of the most widely used open-source data mining suites and software solutions due to its leading-edge technologies and its functional range. Applications of [...]

Changes:

Initial Announcement on mloss.org.


Logo JMLR MLPACK 1.0.9

by rcurtin - July 28, 2014, 20:52:10 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 29908 views, 6011 downloads, 5 subscriptions

Rating Whole StarWhole StarWhole StarWhole Star1/2 Star
(based on 1 vote)

About: A scalable, fast C++ machine learning library, with emphasis on usability.

Changes:
  • GMM initialization is now safer and provides a working GMM when constructed with only the dimensionality and number of Gaussians (#314).
  • Check for division by 0 in Forward-Backward Algorithm in HMMs (#314).
  • Fix MaxVarianceNewCluster (used when re-initializing clusters for k-means) (#314).
  • Fixed implementation of Viterbi algorithm in HMM::Predict() (#316).
  • Significant speedups for dual-tree algorithms using the cover tree (#243, #329) including a faster implementation of FastMKS.
  • Fix for LRSDP optimizer so that it compiles and can be used (#325).
  • CF (collaborative filtering) now expects users and items to be zero-indexed, not one-indexed (#324).
  • 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 (#30).
  • Refactor LRSDP into LRSDP class and standalone function to be optimized (#318).
  • Fix for centering in kernel PCA (#355).
  • 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() (#315).
  • 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 (#320); 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.

Showing Items 1-10 of 536 on page 1 of 54: 1 2 3 4 5 6 Next Last