Communications of the ACM
An efficient agglomerative clustering algorithm using a heap
Pattern Recognition
A continuous approach to inductive inference
Mathematical Programming: Series A and B
Asking questions to minimize errors
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
Generating logical expressions from positive and negative examples via a branch-and-bound approach
Computers and Operations Research
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
The Power of Self-Directed Learning
Machine Learning
Combining Symbolic and Neural Learning
Machine Learning
The Design and Analysis of Computer Algorithms
The Design and Analysis of Computer Algorithms
Rule learning by searching on adapted nets
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
An approach to guided learning of boolean functions
Mathematical and Computer Modelling: An International Journal
An improved branch and bound algorithm for computing k-nearest neighbors
Pattern Recognition Letters
A feature mining based approach for the classification of text documents into disjoint classes
Information Processing and Management: an International Journal
Computers and Operations Research
Computational Biology and Chemistry
A heuristic for mining association rules in polynomial time
Mathematical and Computer Modelling: An International Journal
Hi-index | 0.98 |
Two new heuristics are presented for inferring a small size Boolean function from complete and incomplete examples in polynomial time. These examples are vectors defined in {1,0}^n for the complete case, or in {1, 0,*}^n for the incomplete case (where n is the number of binary attributes or atoms and ''*'' indicates unknown value). Each example is either positive or negative, if it must be accepted or rejected by the target function, respectively. For the incomplete case, however, some examples may be unclassifiable. Moreover, computational results indicate that the proposed heuristics may also be effective in solving very large problems with thousands of examples.