• SHOGUN

  • Referenced in 103 articles [sw03517]
  • support vector machines, hidden Markov models, multiple kernel learning, linear discriminant analysis, and more. Most ... able to deal with several different data classes. We have used this toolbox in several ... already widely adopted in the machine learning community and beyond. SHOGUN is implemented ... stand-alone command line interface. The source code is freely available under the GNU General...
  • EasyMKL

  • Referenced in 6 articles [sw32450]
  • Kernel Learning (MKL) is to combine kernels derived from multiple sources in a data-driven...
  • Morpheo

  • Referenced in 1 article [sw19661]
  • Morpheo is a transparent and secure machine learning platform collecting and analysing large datasets ... fields where data are sensitive. Indeed, it offers strong privacy of data and algorithm ... around data prediction by channelling crypto-money from prediction requests to useful data and algorithms ... designed to handle multiple data sources in a transfer learning approach in order to mutualize...
  • mvlearn

  • Referenced in 1 article [sw39393]
  • learning in Python. As data are generated more and more from multiple disparate sources, multiview ... library which implements the leading multiview machine learning methods. Its simple API closely follows that...
  • ML-flex

  • Referenced in 1 article [sw10521]
  • evidence across multiple algorithms and data sets via ensemble learning. This open-source software package...
  • UJMP

  • Referenced in 3 articles [sw13256]
  • Java Matrix Package (UJMP) is an open source library for dense and sparse matrix computations ... addition to the basic operations like matrix multiplication, matrix inverse or matrix decomposition, it also ... army knife for data processing in Java, tailored to machine learning applications...
  • AI Explainability 360

  • Referenced in 5 articles [sw35201]
  • extensible toolkit for understanding data and machine learning models. As artificial intelligence algorithms make further ... societal applications, there are increasing calls from multiple stakeholders for these algorithms to explain their ... introduce AI Explainability 360, an open-source Python toolkit featuring ten diverse and state ... broader literature on explainability. For data scientists and other users of the toolkit, we have...
  • GenXHC

  • Referenced in 2 articles [sw35559]
  • cross-hybridization in microarray data and a corresponding variational learning method for cross-hybridization compensation ... hybridization noise by taking into account multiple sources for each mRNA expression level measurement ... exon-resolution genome-wide Agilent microarray data set for chromosome 16 of Mus musculus...
  • CIP

  • Referenced in 2 articles [sw12675]
  • open-source high-level function library for (non-linear) curve fitting and data smoothing (with ... medoids, ART-2a) and machine learning (multiple linear/polynomial regression, 3-layer feedforward perceptron-type neural...
  • Evigan

  • Referenced in 5 articles [sw14331]
  • genomes, employing probabilistic inference to integrate multiple sources of gene evidence. The probabilistic model ... predictions; learned parameters encode the relative quality of evidence sources. Since separate training data...
  • HyPER

  • Referenced in 2 articles [sw23917]
  • systems which can incorporate richly structured data sources to improve recommendations. In this paper ... show how a recently introduced statistical relational learning framework can be used to develop ... wide range of information sources. Such sources include multiple user-user and item-item similarity...
  • AutoGluon-Tabular

  • Referenced in 2 articles [sw40229]
  • AutoML for Structured Data. We introduce AutoGluon-Tabular, an open-source AutoML framework that requires ... Python to train highly accurate machine learning models on an unprocessed tabular dataset such ... Tabular succeeds by ensembling multiple models and stacking them in multiple layers. Experiments reveal that ... competitions, AutoGluon beat 99% of the participating data scientists after merely 4h of training...
  • ECNet

  • Referenced in 2 articles [sw29500]
  • open source Python package for creating scalable, retrainable and deployable machine learning projects with ... optimize certain learning criteria (for example, performing optimially on unseen data). Each pool contributes ... data, and these predictions are averaged to calculate the project’s final prediction. Using multiple...
  • ALiPy

  • Referenced in 3 articles [sw33954]
  • toolbox, multiple options are available for each component of the learning framework, including data process ... than 20 state-of-the-art active learning algorithms, ALiPy also supports users to easily ... under different active learning settings, such as AL for multi-label data, AL with noisy ... toolbox is well-documented and open-source on Github, and can be easily installed through...
  • DeepInfer

  • Referenced in 1 article [sw32504]
  • DeepInfer: Open-Source Deep Learning Deployment Toolkit for Image-Guided Therapy. Deep learning models have ... extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models ... registration. However, deploying deep learning models often has a steep learning curve and requires detailed ... between the state-of-the-art machine learning in medical applications and evaluation in clinical...
  • Parray

  • Referenced in 1 article [sw12634]
  • distribution of data over multiple memory devices. The users only need to learn a unified ... typing information contained in the source. This leads to shorter, more portable and maintainable parallel...
  • River

  • Referenced in 1 article [sw39427]
  • learning. It provides multiple state-of-the-art learning methods, data generators/transformers, performance metrics ... evaluators for different stream learning problems. It is the result from the merger ... popular packages for stream learning in Python: Creme and scikit-multiflow. River introduces a revamped ... doing machine learning on streaming data. Additionally, this open source package brings under the same...
  • MUFOS

  • Referenced in 1 article [sw41682]
  • Open-Source MUltiple Tests Corrections and FOrmatted Tables Software (MUFOS). The p value statistic remains ... verisimilitude of experimental hypotheses. However, testing multiple hypotheses poses a problem as Type I error ... most data analysis tools offer limited multiple tests correction options; 2) the learning curve...
  • ThunderGBM

  • Referenced in 1 article [sw39508]
  • solutions to machine learning and data mining problems. However, training and prediction are very expensive ... This article presents an efficient and open source software toolkit called ThunderGBM which exploits ... ranking, and can run on single or multiple GPUs of a machine. Our experimental results...
  • SentiBench

  • Referenced in 2 articles [sw39064]
  • sentiments, including lexical-based and supervised machine learning methods. Despite the vast interest ... used in practice, across multiple datasets originated from different data sources. Such a comparison...