Natively Interpretable Machine Learning and Artificial Intelligence: Preliminary Results and Future Directions.
Christopher J. HazardChristopher FustingMichael ResnickMichael AuerbachMichael MeehanValeri KorobovPublished in: CoRR (2019)
Keyphrases
- future directions
- artificial intelligence
- machine learning
- current trends
- lessons learned
- computational intelligence
- knowledge representation
- computer science
- natural language processing
- web intelligence
- machine learning methods
- machine learning algorithms
- current challenges
- intelligent systems
- open questions
- current status
- knowledge acquisition
- software engineering
- user defined
- decision trees
- learning algorithm
- feature selection
- relational database systems
- information extraction
- knowledge engineering
- data storage
- cognitive science
- classification rules
- learning systems
- human learning
- special issue
- case study
- expert systems
- data warehouse
- text classification