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Showing Items 1-10 of 519 on page 1 of 52: 1 2 3 4 5 6 Next Last

Logo JKernelMachines 2.3

by dpicard - April 17, 2014, 18:42:10 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7794 views, 2159 downloads, 1 subscription

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About: machine learning library in java for easy development of new kernels

Changes:

Version 2.3 (density edition)

  • Cleaned up a lot of thing in density estimators
  • New density estimator algorithms
  • New MKL interface
  • Updated algebra functionalities
  • Better default tunning of parameters in various algorithms

Logo JMLR GPstuff 4.4

by avehtari - April 15, 2014, 15:26:49 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7887 views, 2166 downloads, 1 subscription

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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:

2014-04-11 Version 4.4

New features

  • Monotonicity constraint for the latent function.

    • Riihimäki and Vehtari (2010). Gaussian processes with monotonicity information. Journal of Machine Learning Research: Workshop and Conference Proceedings, 9:645-652.
  • State space implementation for GP inference (1D) using Kalman filtering.

    • For the following covariance functions: Squared-Exponential, Matérn-3/2 & 5/2, Exponential, Periodic, Constant
    • Särkkä, S., Solin, A., Hartikainen, J. (2013). Spatiotemporal learning via infinite-dimensional Bayesian filtering and smoothing. IEEE Signal Processing Magazine, 30(4):51-61.
    • Simo Sarkka (2013). Bayesian filtering and smoothing. Cambridge University Press.
    • Solin, A. and Särkkä, S. (2014). Explicit link between periodic covariance functions and state space models. AISTATS 2014.

Improvements

  • GP_PLOT function for quick plotting of GP predictions
  • GP_IA now warns if it detects multimodal posterior distributions
  • much faster EP with log-Gaussian likelihood (numerical integrals -> analytical results)
  • faster WAIC with GP_IA array (numerical integrals -> analytical results)
  • New demos demonstrating new features etc.
    • demo_minimal, minimal demo for regression and classification
    • demo_kalman1, demo_kalman2
    • demo_monotonic, demo_monotonic2

Plus bug fixes


Logo libAGF 0.9.7

by Petey - April 15, 2014, 04:55:41 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6392 views, 1278 downloads, 1 subscription

About: C++ software for statistical classification, probability estimation and interpolation/non-linear regression using variable bandwidth kernel estimation.

Changes:

New in Version 0.9.7:

  • multi-class classification generalizes class-borders algorithm using a recursive control language
  • hierarchical clustering
  • improved pre-processing

Logo GradMC 2.00

by tur - April 14, 2014, 15:48:48 CET [ BibTeX Download ] 958 views, 347 downloads, 1 subscription

About: GradMC is an algorithm for MR motion artifact removal implemented in Matlab

Changes:

Added support for multi-rigid motion correction.


Logo JMLR Information Theoretical Estimators 0.57

by szzoli - April 10, 2014, 18:35:22 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 32254 views, 6953 downloads, 2 subscriptions

About: ITE (Information Theoretical Estimators) is capable of estimating many different variants of entropy, mutual information, divergence, association measures, cross quantities and kernels on distributions. Thanks to its highly modular design, ITE supports additionally (i) the combinations of the estimation techniques, (ii) the easy construction and embedding of novel information theoretical estimators, and (iii) their immediate application in information theoretical optimization problems.

Changes:
  • Kullback-Leibler divergence estimation based on maximum likelihood estimation + analytical formula in the chosen exponential family: added.

  • A new sampling based entropy estimator with KDE correction on the left/right sides: added.

  • Quick tests: updated with the new estimators.


Logo Somoclu 1.3.1

by peterwittek - April 10, 2014, 06:41:38 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2120 views, 395 downloads, 2 subscriptions

About: Somoclu is a massively parallel implementation of self-organizing maps. It relies on OpenMP for multicore execution, MPI for distributing the workload, and it can be accelerated by CUDA on a GPU cluster. A sparse kernel is also included, which is useful for training maps on vector spaces generated in text mining processes.

Changes:
  • Initial Windows support through GCC on Windows.
  • Better I/O separation for the Python, R, and MATLAB interfaces.
  • Bug fixes: major MPI initialization bug fixed.

Logo MShadow 1.0

by antinucleon - April 10, 2014, 02:57:54 CET [ Project Homepage BibTeX Download ] 171 views, 22 downloads, 1 subscription

About: Lightweight CPU/GPU Matrix/Tensor Template Library in C++/CUDA. Support element-wise expression expand in high performance. Code once, run smoothly on both GPU and CPU

Changes:

Initial Announcement on mloss.org.


Logo CXXNET 0.1

by antinucleon - April 10, 2014, 02:47:08 CET [ Project Homepage BibTeX Download ] 176 views, 19 downloads, 1 subscription

About: CXXNET (spelled as: C plus plus net) is a neural network toolkit build on mshadow(https://github.com/tqchen/mshadow). It is yet another implementation of (convolutional) neural network. It is in C++, with about 1000 lines of network layer implementations, easily configuration via config file, and can get the state of art performance.

Changes:

Initial Announcement on mloss.org.


Logo JMLR dlib ml 18.7

by davis685 - April 10, 2014, 01:47:08 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 69227 views, 12129 downloads, 2 subscriptions

About: This project is a C++ toolkit containing machine learning algorithms and tools that facilitate creating complex software in C++ to solve real world problems.

Changes:

The major new feature in this release is a Python API for training histogram-of-oriented-gradient based object detectors and examples showing how to use this type of detector to perform real-time face detection. Additionally, this release also adds simpler interfaces for learning to solve assignment and multi-target tracking problems.


Logo JMLR MOA Massive Online Analysis Nov-13

by abifet - April 4, 2014, 03:50:20 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 9569 views, 3930 downloads, 1 subscription

About: Massive Online Analysis (MOA) is a real time analytic tool for data streams. It is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA includes a collection of offline and online methods as well as tools for evaluation. In particular, it implements boosting, bagging, and Hoeffding Trees, all with and without Naive Bayes classifiers at the leaves. MOA supports bi-directional interaction with WEKA, the Waikato Environment for Knowledge Analysis, and it is released under the GNU GPL license.

Changes:

New version November 2013


Showing Items 1-10 of 519 on page 1 of 52: 1 2 3 4 5 6 Next Last