• PLANET

  • Referenced in 5 articles [sw15434]
  • mapreduce. Classification and regression tree learning on massive datasets is a common data mining task ... many state of the art tree learning algorithms require training data to reside in memory...
  • Giraph

  • Referenced in 8 articles [sw18984]
  • potential of structured datasets at a massive scale. To learn more, consult the User Docs...
  • Comet

  • Referenced in 3 articles [sw22899]
  • ensemble. This approach is appropriate when learning from massive-scale data that is too large ... random forest. Experiments with two large datasets (5GB and 50GB compressed) show that COMET compares ... both accuracy and training time) to learning on a sub sample of data using...
  • GoGP

  • Referenced in 1 article [sw23912]
  • handle large-scale datasets and to accommodate an online learning setting where data arrive irregularly ... process model that could scale with massive datasets. Our approach is formulated based on alternative...
  • LEAF

  • Referenced in 2 articles [sw34096]
  • massive amounts of data each day. This wealth of data can help to learn models ... federated learning, meta-learning, and multi-task learning. As the machine learning community begins ... learning in federated settings. LEAF includes a suite of open-source federated datasets, a rigorous...
  • BlendTorch

  • Referenced in 1 article [sw35704]
  • synthetic training data. BlendTorch generates data by massively randomizing low-fidelity simulations and takes care ... distributing artificial training data for model learning in real-time. We show that models trained ... those trained on real or photo-realistic datasets...
  • lrtc

  • Referenced in 1 article [sw39611]
  • models, and BERT in particular, are receiving massive attention due to their outstanding performance ... learning techniques for BERT-based classification, addressing a diverse set of AL strategies and datasets...
  • FedGraphNN

  • Referenced in 1 article [sw41837]
  • from graph-structured data. However, centralizing a massive amount of real-world graph data ... commercial competitions. Federated learning (FL), a trending distributed learning paradigm, provides possibilities to solve this ... contains a wide range of datasets from different domains, popular GNN models, and FL algorithms...
  • ACPRISM

  • Referenced in 1 article [sw36168]
  • rules two major drawbacks resulting in a massive set of generated rules, in addition ... groundwater and 16 different well-known datasets using predictive accuracy (%), number of generated rules ... time taken to build the model (learning times). Our experimental results show that the ACPRISM...
  • CGAL

  • Referenced in 402 articles [sw00118]
  • The goal of the CGAL Open Source Project...
  • MapReduce

  • Referenced in 267 articles [sw00546]
  • MapReduce is a new parallel programming model initially...
  • Mathematica

  • Referenced in 6445 articles [sw00554]
  • Almost any workflow involves computing results, and that...
  • Matlab

  • Referenced in 13702 articles [sw00558]
  • MATLAB® is a high-level language and interactive...
  • R

  • Referenced in 10196 articles [sw00771]
  • R is a language and environment for statistical...
  • ScaLAPACK

  • Referenced in 421 articles [sw00830]
  • ScaLAPACK is an acronym for scalable linear algebra...
  • eigs

  • Referenced in 325 articles [sw03702]
  • eigs: Largest eigenvalues and eigenvectors of matrix The...
  • UCI-ml

  • Referenced in 3444 articles [sw04074]
  • UC Irvine Machine Learning Repository. We currently maintain...
  • SVMlight

  • Referenced in 268 articles [sw04076]
  • Description (homepage): SVMlight is an implementation of Vapnik...
  • CPLEX

  • Referenced in 2804 articles [sw04082]
  • IBM® ILOG® CPLEX® offers C, C++, Java, .NET...
  • ARPACK

  • Referenced in 848 articles [sw04218]
  • ARPACK is a collection of Fortran77 subroutines designed...