18462454. MACHINE LEARNING FOR OPTIMIZED LEARNING OF HUMAN-UNDERSTANDABLE LOGICAL RULES FROM MEDICAL OR OTHER DATA simplified abstract (NEC Corporation)
Contents
- 1 MACHINE LEARNING FOR OPTIMIZED LEARNING OF HUMAN-UNDERSTANDABLE LOGICAL RULES FROM MEDICAL OR OTHER DATA
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MACHINE LEARNING FOR OPTIMIZED LEARNING OF HUMAN-UNDERSTANDABLE LOGICAL RULES FROM MEDICAL OR OTHER DATA - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Original Abstract Submitted
MACHINE LEARNING FOR OPTIMIZED LEARNING OF HUMAN-UNDERSTANDABLE LOGICAL RULES FROM MEDICAL OR OTHER DATA
Organization Name
Inventor(s)
Francesco Alesiani of Heidelberg (DE)
Markus Zopf of Heidelberg (DE)
MACHINE LEARNING FOR OPTIMIZED LEARNING OF HUMAN-UNDERSTANDABLE LOGICAL RULES FROM MEDICAL OR OTHER DATA - A simplified explanation of the abstract
This abstract first appeared for US patent application 18462454 titled 'MACHINE LEARNING FOR OPTIMIZED LEARNING OF HUMAN-UNDERSTANDABLE LOGICAL RULES FROM MEDICAL OR OTHER DATA
Simplified Explanation
The patent application describes a machine learning method for learning and applying a rule set from relational data. The method involves receiving a graph that represents the relational data, generating an intermediate representation of the graph, and learning optimized logical rules based on the intermediate representation.
- The method receives a graph that represents relational data, where nodes represent elements and edges represent relationships between the elements.
- The method generates an intermediate representation of the graph by mapping features of the nodes and edges to an intermediate vector representation.
- The method learns optimized logical rules by defining a maximum satisfiability (MAX-SAT) problem for the graph and estimating a gradient around a solution of the MAX-SAT problem.
- The optimized logical rules are then applied to a new graph, which can be used in various machine learning tasks such as disease prediction or decision-making support.
Potential Applications
- Disease prediction using medical data.
- Optimization of machine learning tasks.
- Decision-making support.
Problems Solved
- Learning and applying a rule set from relational data.
- Optimizing machine learning tasks.
- Supporting decision-making using relational data.
Benefits
- Improved accuracy and efficiency in learning and applying rules from relational data.
- Enhanced performance in machine learning tasks.
- Better decision-making support based on relational data.
Original Abstract Submitted
A machine learning method for learning and applying a rule set from relational data includes receiving a graph representing relational data, wherein nodes represent elements of the graph, and edges represent relationships between nodes, and generating an intermediate representation of the graph by mapping features of the nodes and edges of the graph to an intermediate vector representation. Optimized logical rules that define the nodes and edges of the graph based on the intermediate vector representation are learned by: defining a maximum satisfiability (MAX-SAT) problem for the graph; and estimating a gradient around a solution of the MAX-SAT problem to produce the optimized logical rules, which are applied to a new graph. The data can be medical data and the graph can be used in a machine-learning task, such as using the medical data for disease prediction, for optimization of the machine-learning task and/or to support decision-making.
