GeezerVison is showing mloss.ratschlab.org — mloss | All entries ARCHIVE Snapshot of mloss.org – machine learning open source software as of 2025: preserved, not maintained. Kept by ETH Zurich · Legal notice & privacy
All entries.
Search
-
Manage
- Loginto submit a new project
Sort by
Filter by
Latest Thoughts
- MLOSS workshop at ICML 2015: Open Ecosystems
by
Antti Honkela
on March 30, 2015
- A third of the top 100 papers are about software
by
Cheng Soon Ong
on October 30, 2014
- Open Machine Learning Workshop
by
Cheng Soon Ong
on July 28, 2014
- Machine Learning Distro
by
Cheng Soon Ong
on July 22, 2014
- Google Summer of Code 2014
by
Cheng Soon Ong
on June 3, 2014
- Reproducibility is not simple
by
Cheng Soon Ong
on March 30, 2014
- Keynotes at ACML 2013
by
Cheng Soon Ong
on November 14, 2013
- What does the “OSS” in MLOSS mean?
by
Mark Reid
on September 1, 2013
- Code review for science
by
Cheng Soon Ong
on August 14, 2013
- GSoC 2013
by
Cheng Soon Ong
on April 9, 2013
No forum yet.
About:
Hyperstream is a large-scale, flexible and robust software package for processing streaming data.
Changes:
python 3 support; new API; bug fixes and enhancements
|
About:
Easy differential privacy
Changes:
Initial Announcement on mloss.org.
|
About:
glyph is a python 3 library based on deap providing abstraction layers for symbolic regression problems.
Changes:
Initial Announcement on mloss.org.
|
About:
Large scale, distributed graph processing made easy.
Changes:
Graph partitioning methods
APSP approximation method
Performance improvements
License change
Bug fixes
|
About:
A Matlab benchmarking toolbox for online and adaptive regression with kernels.
Changes:
-
Changes in algorithms' Matlab class format
-
New algorithms
-
Minor improvements and bug fixes
|
About:
DynaML is a Scala environment for conducting research and education in Machine Learning. DynaML comes packaged with a powerful library of classes implementing predictive models and a Scala REPL where one can not only build custom models but also play around with data work-flows.
Changes:
Initial Announcement on mloss.org.
|
About:
Extending Lasso Model Fitting to Big Data
Changes:
Fetched by r-cran-robot on 2018-09-01 00:00:04.365069
|
About:
Code for Calibrated AdaMEC for binary cost-sensitive classification. The method is just AdaBoost that properly calibrates its probability estimates and uses a cost-sensitive (i.e. risk-minimizing) decision threshold to classify new data.
Changes:
Updated license information
|
|
- Operating System:
Platform Independent,
Platform Agnostic
- Data Formats:
Binary
- Tags:
Ensembles,
Adaboost,
Boosting,
Ensemble Of Classifiers,
Ensemble Methods,
Ensemble Learning,
Ensemble Model,
Calibration,
Class Imbalance,
Cost Sensitive,
Minimum Expected Cost,
Risk Minimization
|
|
About:
MSVMpack is a Multi-class Support Vector Machine (M-SVM) package. It is dedicated to SVMs which can handle more than two classes without relying on decomposition methods and implements the four M-SVM models from the literature: Weston and Watkins M-SVM, Crammer and Singer M-SVM, Lee, Lin and Wahba M-SVM, and the M-SVM2 of Guermeur and Monfrini.
Changes:
-
Fix compilation error with recent gcc
|
About:
OpenNN is a software library written in C++ for advanced analytics. It implements neural networks, the most successful machine learning method.
The library has been designed to learn from both data sets and mathematical models.
Changes:
New algorithms, correction of bugs.
|
About:
A mutual information library for C and Mex bindings for MATLAB. Aimed at feature selection, and provides simple methods to calculate mutual information, conditional mutual information, entropy, conditional entropy, Renyi entropy/mutual information, and weighted variants of Shannon entropies/mutual informations. Works with discrete distributions, and expects column vectors of features.
Changes:
Fixed a Windows compilation bug. MIToolbox v3 should now compile using Visual Studio.
|
About:
A native Python, scikit-compatible, implementation of a variety of multi-label classification algorithms.
Changes:
-
a general matrix-based label space clusterer has been added which can cluster the output space using any scikit-learn compatible clusterer (incl. k-means)
-
support for more single-class and multi-class classifiers you can now use problem transformation approaches with your favourite neural networks/deep learning libraries: theano, tensorflow, keras, scikit-neuralnetworks
-
support for label powerset based stratified kfold added
-
graph-tool clusterer supports weighted graphs again and includes stochastic blockmodel calibration
-
bugs were fixed in: classifier chains and hierarchical neuro fuzzy clasifiers
|
About:
The new R package opusminer provides an R interface to the OPUS Miner algorithm (implemented in C++) for finding the key associations in transaction data efficiently, in the form of self-sufficient itemsets, using either leverage or lift.
Changes:
Initial Announcement on mloss.org.
|
About:
ADENINE (A Data ExploratioN pIpeliNE) is a machine learning framework for data exploration that encompasses state-of-the-art techniques for missing values imputing, data preprocessing, unsupervised feature learning and clustering tasks.
Changes:
-
Adenine can now distribute the execution of its pipelines on multiple machines via MPI
-
kNN data imputing strategy is now implemented
-
added python 2.7 and 3.5 support
-
stability improved and bug fixed
|
About:
The proposed hashing algorithm leverages the bootstrap sampling idea and integrates it with PCA, resulting in a new projection method called Bagging PCA Hashing.
Changes:
Initial Announcement on mloss.org.
|
About:
This is an online hashing algorithm which can handle the stream data with low computational cost.
Changes:
Initial Announcement on mloss.org.
|
About:
A library of scalable Bayesian generalised linear models with fancy features
Changes:
-
1.0 release!
-
Now there is a random search phase before optimization of all hyperparameters in the regression algorithms. This improves the performance of revrand since local optima are more easily avoided with this improved initialisation
-
Regression regularizers (weight variances) associated with each basis object, this approximates GP kernel addition more closely
-
Random state can be set for all random objects
-
Numerous small improvements to make revrand production ready
-
Final report
-
Documentation improvements
|
About:
NaN-toolbox is a statistics and machine learning toolbox for handling data with and without missing values.
Changes:
Changes in v.3.1.2
- improve configuration and build system
- support of more platforms (including Octave 4.2.0) improved
Changes in v.3.0.3
- improve compatibility for Octave on Windows
Changes in v.3.0.1
- fix packaging for octave
Changes in v.2.8.5
- bug fix: trimmean
- compiler support for gcc-5 and clang
- fix typos
For details see the CHANGELOG at
http://pub.ist.ac.at/~schloegl/matlab/NaN/CHANGELOG
|
|
- Operating System:
Agnostic
- Data Formats:
Matlab,
Arff,
Octave,
Csv,
Dlm,
Stata,
Sas,
Xpt
- Tags:
Classification,
Multi Class,
Machine Learning,
Missing Data,
Statistics,
Weighting
|
|
About:
LogReg-Crowds is a collection of Julia implementations of various approaches for learning a logistic regression model multiple annotators and crowds, namely the works of Raykar et al. (2010), Rodrigues et al. (2013) and Dawid and Skene (1979).
Changes:
Initial Announcement on mloss.org.
Added GitHub page.
|
About:
MA-sLDAr is a C++ implementation of the supervised topic models with response variables provided by multiple annotators with different levels of expertise.
Changes:
Initial Announcement on mloss.org.
|
|
RSS Feed - Blog
