PARADIGMS FOR REALIZING MACHINE LEARNING ALGORITHMS

被引：13
|
作者：
Agneeswaran, Vijay Srinivas [1 ]
Tonpay, Pranay [1 ]
Tiwary, Jayati [1 ]
机构：
[1] Impetus Infotech India Private Ltd, Bangalore 560103, Karnataka, India
来源：
BIG DATA | 2013年 / 1卷 / 04期
关键词：
D O I：
10.1089/big.2013.0006
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
The article explains the three generations of machine learning algorithms-with all three trying to operate on big data. The first generation tools are SAS, SPSS, etc., while second generation realizations include Mahout and RapidMiner (that work over Hadoop), and the third generation paradigms include Spark and GraphLab, among others. The essence of the article is that for a number of machine learning algorithms, it is important to look beyond the Hadoop's Map- Reduce paradigm in order to make them work on big data. A number of promising contenders have emerged in the third generation that can be exploited to realize deep analytics on big data.
引用
收藏
页码：BD207 / BD214
页数：8
相关论文
共 50 条
  • [31] Prediction of Arrhythmia with Machine Learning Algorithms
    Gursoy, Gunes
    Varol, Asaf
    9TH INTERNATIONAL SYMPOSIUM ON DIGITAL FORENSICS AND SECURITY (ISDFS'21), 2021,
  • [32] A Conjectural Study on Machine Learning Algorithms
    Sankar, Abijith
    Bharathi, P. Divya
    Midhun, M.
    Vijay, K.
    Kumar, T. Senthil
    PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON SOFT COMPUTING SYSTEMS, ICSCS 2015, VOL 1, 2016, 397 : 105 - 116
  • [33] Experiments on the Generalization of Machine Learning Algorithms
    Franz, Arthur
    ARTIFICIAL GENERAL INTELLIGENCE, AGI 2021, 2022, 13154 : 75 - 85
  • [34] Image Inpainting by Machine Learning Algorithms
    Wan, Wei
    Leonov, Ivan
    PATTERN RECOGNITION AND IMAGE ANALYSIS, 2024, 34 (02) : 237 - 243
  • [35] A Lazy Approach for Machine Learning Algorithms
    Galván, Inés M.
    Valls, José M.
    Lecomte, Nicolas
    Isasi, Pedro
    IFIP Advances in Information and Communication Technology, 2009, 296 : 517 - 522
  • [36] Enclosing machine learning: concepts and algorithms
    Xun-Kai Wei
    Ying-Hong Li
    Yu-Fei Li
    Dong-Fang Zhang
    Neural Computing and Applications, 2008, 17 : 237 - 243
  • [37] Machine learning algorithms for event detection
    Margineantu, Dragos
    Wong, Weng-Keen
    Dash, Denver
    MACHINE LEARNING, 2010, 79 (03) : 257 - 259
  • [38] Genetic algorithms and machine learning.
    Venturini, G.
    Revue d'Intelligence Artificielle, 1996, 10 (2-3) : 345 - 387
  • [39] A Lazy Approach for Machine Learning Algorithms
    Galvan, Ines M.
    Valls, Jose M.
    Lecomte, Nicolas
    Isasi, Pedro
    ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS III, 2009, : 517 - 522
  • [40] Machine Learning Algorithms Understanding and Implication
    Garg, Sumit
    Khurana, Aditi
    Ravindra, Shubha G.
    Kumar, Rakesh
    Bal, Chandrasekhar
    JOURNAL OF NUCLEAR MEDICINE, 2024, 65
12345