- Referenced in 3086 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...
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- C4.5: programs for machine learning. (C4.5 has been superseded by C5.0...
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- LIBSVM has gained wide popularity in machine learning and many other areas. In this article...
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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...
- Referenced in 321 articles
- organization for the purposes of conducting machine learning and deep neural networks research...
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- Knowledge Analysis. WEKA is a popular machine learning workbench with a development life of nearly...
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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...
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- 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...
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- 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...
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- package kernlab: Kernel-based Machine Learning Lab. Kernel-based machine learning methods for classification, regression ... Among other methods kernlab includes Support Vector Machines, Spectral Clustering, Kernel PCA, Gaussian Processes...
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- 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...
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- from the training text data and then learns vector representation of words. The resulting word ... many natural language processing and machine learning applications...
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- Such structured constraints appear pervasively in machine learning applications, including low-rank matrix completion, sensor...
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- software engineering, database and web design, machine learning, and in visual interfaces for other technical...
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- 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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- 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...
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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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- efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost...
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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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- effective for the hyperparameter optimization of machine learning algorithms, scaling better to high dimensions...