Making the Most of What You've Got: Using Models and Data to Improve Learning Rate and Prediction Accuracy
Abstract
Prediction and classification in areas such as engineering, medicine, and applied expert systems often relies on two sources of knowledge: actual data and a model of the domain. Recent efforts in machine learning (Ourston 1991) (Towell, Shavlik, & Noordewier 1990) have developed techniques that take advantage of both sources, but the methods are often tied to particular types of models and induction techniques. We propose two general techniques that allow induction methods, C4.5(Quinlan 1993) in our case, to take advantage of an available model(Ortega 1994).
Cite
Text
Ortega. "Making the Most of What You've Got: Using Models and Data to Improve Learning Rate and Prediction Accuracy." AAAI Conference on Artificial Intelligence, 1994.Markdown
[Ortega. "Making the Most of What You've Got: Using Models and Data to Improve Learning Rate and Prediction Accuracy." AAAI Conference on Artificial Intelligence, 1994.](https://mlanthology.org/aaai/1994/ortega1994aaai-making/)BibTeX
@inproceedings{ortega1994aaai-making,
title = {{Making the Most of What You've Got: Using Models and Data to Improve Learning Rate and Prediction Accuracy}},
author = {Ortega, Julio},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {1994},
pages = {1483},
url = {https://mlanthology.org/aaai/1994/ortega1994aaai-making/}
}