• SVMTorch

  • Referenced in 67 articles [sw12121]
  • Joachims [“Making large-scale support vector machine learning practical”, in: B. Schölkopf, C. Burges ... algorithm, one can now efficiently solve large-scale regression problems (more than 20000 examples). Comparisons ... large-scale regression problems from G. Flake and S. Lawrence [Mach. Learn ... decomposition method for support vector machines (Tech. Rep.). National Taiwan University (2000)], we show that...
  • SHOGUN

  • Referenced in 103 articles [sw03517]
  • machine learning toolbox, called SHOGUN, which is designed for unified large-scale learning ... settings. It offers a considerable number of machine learning models such as support vector machines...
  • SimpleMKL

  • Referenced in 67 articles [sw12290]
  • support vector machine, an efficient and general multiple kernel learning algorithm, based on semi-infinite ... tractable for large-scale problems, by iteratively using existing support vector machine code. However ... that encourages sparse kernel combinations. Apart from learning the combination, we solve a standard...
  • LAMG

  • Referenced in 34 articles [sw06551]
  • graphs arise in large-scale computational applications such as semisupervised machine learning; spectral clustering...
  • MLlib

  • Referenced in 25 articles [sw15430]
  • large-scale data processing that is well-suited for iterative machine learning tasks. In this...
  • SVMlight

  • Referenced in 264 articles [sw04076]
  • implementation of Vapnik’s Support Vector Machine [Vapnik, 1995] for the problem of pattern recognition ... regression, and for the problem of learning a ranking function. The optimization algorithms used ... algorithm for learning ranking functions [Joachims, 2002c]. The goal is to learn a function from ... version includes an algorithm for training large-scale transductive SVMs. The algorithm proceeds by solving...
  • Spark

  • Referenced in 40 articles [sw23653]
  • have been highly successful in implementing large-scale data-intensive applications on commodity clusters. However ... parallel operations. This includes many iterative machine learning algorithms, as well as interactive data analysis...
  • DryadLINQ

  • Referenced in 7 articles [sw23712]
  • graph analysis, large-scale log mining, and machine learning. We show that excellent absolute performance...
  • BindsNET

  • Referenced in 1 article [sw30217]
  • neural networks, specifically geared towards machine learning and reinforcement learning. Our software, called BindsNET, enables ... enabling fast CPU and GPU computation for large spiking networks. The BindsNET framework ... spiking networks for large-scale machine learning experimentation, and show some simple examples...
  • SGDLibrary

  • Referenced in 6 articles [sw26680]
  • field of machine learning (ML). One promising approach for large-scale data...
  • Nieme

  • Referenced in 1 article [sw14441]
  • introduce Nieme, a machine learning library for large-scale classification, regression and ranking. Nieme relies ... LeCun et al., 2006) which unifies several learning algorithms ranging from simple perceptrons to recent ... machine or l1-regularized maximum entropy models. This framework also unifies batch and stochastic learning ... situations, but is particularly interesting for large-scale learning tasks where both the examples...
  • LIBOL

  • Referenced in 9 articles [sw10887]
  • state-of-the-art online learning algorithms for large-scale online classification tasks. We have ... users. LIBOL is not only a machine learning toolbox, but also a comprehensive experimental platform...
  • PNKH-B

  • Referenced in 2 articles [sw40451]
  • commonly the case in large-scale parameter estimation, machine learning, and image processing. In each...
  • MFclass

  • Referenced in 2 articles [sw34570]
  • accelerating the prediction of large-scale computational models. Machine learning techniques typically rely on large...
  • LIBS2ML

  • Referenced in 1 article [sw28299]
  • Order Machine Learning Algorithms. LIBS2ML is a library based on scalable second order learning algorithms ... solving large-scale problems, i.e., big data problems in machine learning. LIBS2ML has been developed ... very slow and not suitable for large-scale learning, or are in C/C++ which does ... provides machine learning practitioners an effective tool to deal with the large-scale learning problems...
  • FASTCLIME

  • Referenced in 12 articles [sw10889]
  • scalable and sophisticated tool for solving large-scale linear programs. As an illustrative example ... useful to statisticians and machine learning researchers for solving a wide range of problems...
  • MLaut

  • Referenced in 1 article [sw27171]
  • ecosystem. MLaut automates large-scale evaluation and benchmarking of machine learning algorithms on a large ... provides a high-level workflow interface to machine algorithm algorithms, implements a local back ... learning architecture. As a principal test case for MLaut, we conducted a large-scale supervised ... performance of a number of machine learning algorithms - to our knowledge also the first larger...
  • ZOOpt

  • Referenced in 2 articles [sw22396]
  • optimization problems in machine learning, addressing high-dimensional, noisy, and large-scale problems. The toolbox ... tools in real-world machine learning tasks...
  • LiblineaR

  • Referenced in 5 articles [sw25718]
  • machine learning (available at ). ’LIBLINEAR’ is a simple library for solving large ... scale regularized linear classification and regression. It currently supports L2-regularized classification (such as logistic...
  • DGL-KE

  • Referenced in 3 articles [sw34088]
  • Training Knowledge Graph Embeddings at Scale. Knowledge graphs (KGs) are data structures that store information ... approach of using KGs in various machine learning tasks is to compute knowledge graph embeddings ... scalable package for learning large-scale knowledge graph embeddings. The package is implemented ... developers can run DGL-KE on CPU machine, GPU machine, as well as clusters with...