Machine learning an artificial intelligence approach volume II
Machine learning an artificial intelligence approach volume II
Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
Implementing Dempster's rule for hierarchial evidence
Artificial Intelligence
Computer
International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Incremental version-space merging: a general framework for concept learning
Incremental version-space merging: a general framework for concept learning
Machine learning: an artificial intelligence approach volume III
Machine learning: an artificial intelligence approach volume III
C4.5: programs for machine learning
C4.5: programs for machine learning
Generating fuzzy membership functions: a monotonic neural network model
Fuzzy Sets and Systems
Construction of fuzzy classification systems with rectangular fuzzy rules using genetic algorithms
Fuzzy Sets and Systems - Special issue on fuzzy methods for computer vision and pattern recognition
Machine Learning
Induction of fuzzy decision trees
Fuzzy Sets and Systems
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Induction of fuzzy rules and membership functions from training examples
Fuzzy Sets and Systems
A fuzzy inductive learning strategy for modular rules
Fuzzy Sets and Systems
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Expert Systems: Principles and Programming
Expert Systems: Principles and Programming
Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence)
Fuzzy logic = computing with words
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
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Most fuzzy controllers and fuzzy expert systems must predefine membership functions and fuzzy inference rules to map numeric data into fuzzy linguistic values and to make fuzzy reasoning work. Recently, fuzzy systems that automatically derive fuzzy if-then rules from numeric data have been developed. In this paper, we propose a new learning method to automatically derive membership functions and fuzzy if-then rules from a set of given training examples. This method adopts a different way in building initial membership functions, thus making the learning procedure simpler than that used in [10]. Experiments are also made to show the performance of the newly proposed learning algorithm.