Generalizing Data in Natural Language

Authors:
Ryszard S. Michalski;Janusz Wojtusiak
Affiliations:
Machine Learning and Inference Laboratory, George Mason University, Fairfax, VA 22030, USA and Institute of Computer Science, Polish Academy of Science, Warsaw, Poland;Machine Learning and Inference Laboratory, George Mason University, Fairfax, VA 22030, USA
Venue:
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Year:
2007

Citing 10
Cited 0

Variable precision logic

Artificial Intelligence
C4.5: programs for machine learning

C4.5: programs for machine learning
Rough Sets: Theoretical Aspects of Reasoning about Data

Rough Sets: Theoretical Aspects of Reasoning about Data
Knowledge discovery from data?

IEEE Intelligent Systems
The CN2 Induction Algorithm

Machine Learning
Unified algorithm for undirected discovery of exception rules: Research Articles

International Journal of Intelligent Systems - Knowledge Discovery: Dedicated to Jan M. Żytkow
Real versus Template-Based Natural Language Generation: A False Opposition?

Computational Linguistics
An empirical comparison of supervised learning algorithms

ICML '06 Proceedings of the 23rd international conference on Machine learning
The AQ21 Natural Induction Program for Pattern Discovery: Initial Version and its Novel Features

ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Constructive induction: a version space-based approach

IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper concerns the development of a new direction in machine learning, called natural induction, which requires from computer-generated knowledge not only to have high predictive accuracy, but also to be in human-oriented forms, such as natural language descriptions and/or graphical representations. Such forms facilitate understanding and acceptance of the learned knowledge, and making mental models that are useful for decision making. An initial version of the AQ21-NI program for natural induction and its several novel features are briefly described. The performance of the program is illustrated by an example of deriving medical diagnostic rules from micro-array data.