AMIDST: A Java toolbox for scalable probabilistic machine learning.
Andrés R. MasegosaAna M. MartínezDarío Ramos-LópezRafael CabañasAntonio SalmerónHelge LangsethThomas D. NielsenAnders L. MadsenPublished in: Knowl. Based Syst. (2019)
Keyphrases
- machine learning
- lightweight
- learning algorithm
- artificial intelligence
- information extraction
- probabilistic model
- bayesian networks
- database
- programming language
- object oriented
- decision trees
- data mining
- generative model
- probability distribution
- explanation based learning
- source code
- open source
- learning systems
- probability theory
- machine learning algorithms
- semi supervised learning
- learning tasks
- inductive learning
- computational intelligence
- web scale
- data analysis
- pattern recognition
- software package
- database systems
- machine learning approaches
- java programs
- uncertain data
- inductive logic programming
- posterior probability
- development environment
- handling uncertainty
- information theoretic
- bit rate
- model selection
- text classification
- data driven
- web applications
- natural language processing
- semi supervised
- natural language
- high level
- case study
- web services
- computer vision
- data sets