• Camera Calibration

  • Referenced in 51 articles [sw13537]
  • point cloud processing. With machine learning based frameworks, you can train object detection, object recognition...
  • mlbench

  • Referenced in 35 articles [sw08134]
  • package mlbench: Machine Learning Benchmark Problems. A collection of artificial and real-world machine learning...
  • Torch

  • Referenced in 34 articles [sw05407]
  • Torch is a machine learning library written in C++ that works on most Unix/Linux platforms ... that you can add your own machine learning algorithms...
  • Jupyter

  • Referenced in 48 articles [sw21266]
  • transformation, numerical simulation, statistical modeling, machine learning and much more...
  • CMAR

  • Referenced in 47 articles [sw28406]
  • databases from the UCI machine learning database repository show that CMAR is consistent, highly effective...
  • Spark

  • Referenced in 31 articles [sw23653]
  • parallel operations. This includes many iterative machine learning algorithms, as well as interactive data analysis ... objects partitioned across a set of machines that can be rebuilt if a partition ... outperform Hadoop by 10x in iterative machine learning jobs, and can be used to interactively...
  • HOGWILD

  • Referenced in 42 articles [sw28396]
  • performance on a variety of machine learning tasks. Several researchers have recently proposed schemes...
  • Daikon

  • Referenced in 42 articles [sw04319]
  • executions. Dynamic invariant detection is a machine learning technique that can be applied to arbitrary...
  • XGBoost

  • Referenced in 41 articles [sw21035]
  • efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost...
  • Pse-in-One

  • Referenced in 41 articles [sw22407]
  • vectors can be easily combined with machine-learning algorithms to develop computational predictors and analysis...
  • mlr

  • Referenced in 26 articles [sw12357]
  • Machine Learning in R. Interface to a large number of classification and regression techniques, including ... machine-readable parameter descriptions. There is also an experimental extension for survival analysis, clustering ... general, example-specific cost-sensitive learning. Generic resampling, including cross-validation, bootstrapping and subsampling. Hyperparameter ... learners with additional operations common in machine learning, also allowing for easy nested resampling. Most...
  • Prodigy

  • Referenced in 37 articles [sw20686]
  • basis for research in planning, machine learning, apprentice-type knowledge-refinement interfaces, and expert systems...
  • MOA

  • Referenced in 36 articles [sw11966]
  • blog). It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept...
  • OP-ELM

  • Referenced in 21 articles [sw12171]
  • optimally pruned extreme learning machine. In this brief, the optimally pruned extreme learning machine ... based on the original extreme learning machine (ELM) algorithm with additional steps to make...
  • Apache Spark

  • Referenced in 34 articles [sw28418]
  • DataFrames, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing...
  • MLlib

  • Referenced in 19 articles [sw15430]
  • MLlib: machine learning in apache spark. Apache Spark is a popular open-source platform ... that is well-suited for iterative machine learning tasks. In this paper we present MLlib ... Spark’s open-source distributed machine learning library. MLlib provides efficient functionality for a wide ... development of end-to-end machine learning pipelines. MLlib has experienced a rapid growth...
  • GPy

  • Referenced in 20 articles [sw14302]
  • written in python, from the Sheffield machine learning group. Gaussian processes underpin range of modern ... machine learning algorithms. In GPy, we’ve used python to implement a range of machine...
  • GPML

  • Referenced in 32 articles [sw12890]
  • Gaussian processes for machine learning (GPML) toolbox. The GPML toolbox provides a wide range...
  • GraphLab

  • Referenced in 22 articles [sw12830]
  • GraphLab: A New Framework For Parallel Machine Learning. Designing and implementing efficient, provably correct parallel ... machine learning (ML) algorithms is challenging. Existing high-level parallel abstractions like MapReduce are insufficiently...
  • MaSh

  • Referenced in 21 articles [sw08206]
  • MaSh: Machine Learning for Sledgehammer. Sledgehammer integrates automatic theorem provers in the proof assistant Isabelle/HOL ... goal. We introduce MaSh, an alternative that learns from successful proofs. New challenges arose from ... workflow, so that they benefit from machine learning without having to install software...