The Apache Mahout™ machine learning library’s goal is to build scalable machine learning libraries. Apache Mahout: Scalable machine learning for everyone:

References in zbMATH (referenced in 9 articles , 1 standard article )

Showing results 1 to 9 of 9.
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  1. Anil, Robin; Capan, Gokhan; Drost-Fromm, Isabel; Dunning, Ted; Friedman, Ellen; Grant, Trevor; Quinn, Shannon; Ranjan, Paritosh; Schelter, Sebastian; Yılmazel, Özgür: Apache Mahout: machine learning on distributed dataflow systems (2020)
  2. Rohan Anand, Joeran Beel: Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization (2020) arXiv
  3. Alexander Foss; Marianthi Markatou: kamila: Clustering Mixed-Type Data in R and Hadoop (2018) not zbMATH
  4. Andrea Esuli, Tiziano Fagni, Alejandro Moreo Fernandez: JaTeCS an open-source JAva TExt Categorization System (2017) arXiv
  5. Meng, Xiangrui; Bradley, Joseph; Yavuz, Burak; Sparks, Evan; Venkataraman, Shivaram; Liu, Davies; Freeman, Jeremy; Tsai, Db; Amde, Manish; Owen, Sean; Xin, Doris; Xin, Reynold; Franklin, Michael J.; Zadeh, Reza; Zaharia, Matei; Talwalkar, Ameet: MLlib: machine learning in Apache Spark (2016)
  6. Kim, Milhan; Lee, Youngjun; Park, Ho-Hyun; Hahn, Sang June; Lee, Chan-Gun: Computational fluid dynamics simulation based on hadoop ecosystem and heterogeneous computing (2015)
  7. Philip Chen, C. L.; Zhang, Chun-Yang: Data-intensive applications, challenges, techniques and technologies: a survey on big data (2014) ioport
  8. Weihs, Claus; Mersmann, Olaf; Ligges, Uwe: Foundations of statistical algorithms. With references to R packages (2014)
  9. Lee, Joonseok; Sun, Mingxuan; Lebanon, Guy: PREA: personalized recommendation algorithms toolkit (2012) ioport