An efficient algorithm for learning to rank from preference graphs
Machine Learning
Using Rest Class and Control Paradigms for Brain Computer Interfacing
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
From data to knowledge to discoveries: Artificial intelligence and scientific workflows
Scientific Programming
Reusable components for partitioning clustering algorithms
Artificial Intelligence Review
Implementation and integration of algorithms into the KEEL data-mining software tool
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
The SHOGUN Machine Learning Toolbox
The Journal of Machine Learning Research
TunedIT.org: system for automated evaluation of algorithms in repeatable experiments
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Waffles: A Machine Learning Toolkit
The Journal of Machine Learning Research
MULAN: A Java Library for Multi-Label Learning
The Journal of Machine Learning Research
Towards programming languages for machine learning and data mining
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Making data analysis expertise broadly accessible through workflows
Proceedings of the 6th workshop on Workflows in support of large-scale science
An architecture for component-based design of representative-based clustering algorithms
Data & Knowledge Engineering
Workshop on reproducibility and replication in recommender systems evaluation: RepSys
Proceedings of the 7th ACM conference on Recommender systems
The Remote Sensing and GIS Software Library (RSGISLib)
Computers & Geosciences
Component-based decision trees for classification
Intelligent Data Analysis
Evolutionary approach for automated component-based decision tree algorithm design
Intelligent Data Analysis - Business Analytics and Intelligent Optimization
Computers in Biology and Medicine
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Open source tools have recently reached a level of maturity which makes them suitable for building large-scale real-world systems. At the same time, the field of machine learning has developed a large body of powerful learning algorithms for diverse applications. However, the true potential of these methods is not used, since existing implementations are not openly shared, resulting in software with low usability, and weak interoperability. We argue that this situation can be significantly improved by increasing incentives for researchers to publish their software under an open source model. Additionally, we outline the problems authors are faced with when trying to publish algorithmic implementations of machine learning methods. We believe that a resource of peer reviewed software accompanied by short articles would be highly valuable to both the machine learning and the general scientific community.