- Referenced in 1869 articles
- Irvine Machine Learning Repository. We currently maintain 251 data sets as a service ... machine learning community. You may view all data sets through our searchable interface ... site for the Repository. The UCI Machine Learning Repository is a collection of databases, domain ... generators that are used by the machine learning community for the empirical analysis of machine...
- Referenced in 875 articles
- C4.5: programs for machine learning. (C4.5 has been superseded by C5.0...
- Referenced in 603 articles
- LIBSVM has gained wide popularity in machine learning and many other areas. In this article...
- Referenced in 135 articles
- Knowledge Analysis. WEKA is a popular machine learning workbench with a development life of nearly...
- Referenced in 57 articles
- SHOGUN machine learning toolbox We have developed a machine learning toolbox, called SHOGUN, which ... offers a considerable number of machine learning models such as support vector machines, hidden Markov ... models, multiple kernel learning, linear discriminant analysis, and more. Most of the specific algorithms ... already widely adopted in the machine learning community and beyond. SHOGUN is implemented...
- Referenced in 93 articles
- LERS – a system for learning from examples based on rough sets. The paper presents ... choice to use the machine learning approach or the knowledge acquisition approach. In the first...
- Referenced in 59 articles
- AntNet, a novel approach to the adaptive learning of routing tables in communications networks. AntNet ... algorithms coming from the telecommunications and machine learning fields. The algorithms’ performance is evaluated over...
- Referenced in 42 articles
- open source computer vision and machine learning software library. OpenCV was built to provide ... applications and to accelerate the use of machine perception in the commercial products. Being ... computer vision and machine learning algorithms. These algorithms can be used to detect and recognize...
- Referenced in 56 articles
- Joachims [“Making large-scale support vector machine learning practical”, in: B. Schölkopf, C. Burges ... from G. Flake and S. Lawrence [Mach. Learn. 46, 271–290 (2002; Zbl 0998.68107)] yielded ... decomposition method for support vector machines (Tech. Rep.). National Taiwan University (2000)], we show that...
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- machine learning library in C++. We present MLC++, a library of C++ classes and tools ... supervised machine learning. While MLC++ provides general learning algorithms that can be used...
- Referenced in 49 articles
- software engineering, database and web design, machine learning, and in visual interfaces for other technical...
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- kernlab: Kernel-based Machine Learning Lab. Kernel-based machine learning methods for classification, regression, clustering ... Among other methods kernlab includes Support Vector Machines, Spectral Clustering, Kernel PCA, Gaussian Processes...
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- point cloud processing. With machine learning based frameworks, you can train object detection, object recognition...
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- SPASS ATP systems) with a machine learning component (now the SNoW system used ... naive Bayesian learning mode). Its intended use is in large theories, i.e. on a large ... cycles of theorem proving followed by machine learning from successful proofs, using the learned information...
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- hard MML problems, sometimes assisted by machine learning. It is shown that on the nonarithmetical ... premises are selected by a machine-learning system trained on previous proofs...
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- executions. Dynamic invariant detection is a machine learning technique that can be applied to arbitrary...
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- Scikit-learn: machine learning in python. Scikit-learn is a Python module integrating a wide ... range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised ... problems. This package focuses on bringing machine learning to non-specialists using a general-purpose...
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- accompany Kevin Murphy’s textbook Machine learning: a probabilistic perspective, but can also be used ... unified conceptual and software framework encompassing machine learning, graphical models, and Bayesian statistics (hence...
- Referenced in 45 articles
- associated predictor in supervised learning settings. For the support vector machine, an efficient and general ... multiple kernel learning algorithm, based on semi-infinite linear programming, has been recently proposed. This ... problems, by iteratively using existing support vector machine code. However, it turns out that this ... that encourages sparse kernel combinations. Apart from learning the combination, we solve a standard...
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- Such structured constraints appear pervasively in machine learning applications, including low-rank matrix completion, sensor...