Info on Servo Data Base
1. Title: Servo Data
2. Sources
(a) Created by: Karl Ulrich (MIT) in 1986
(b) Donor: Ross Quinlan
(c) Date: May 1993
3. Past Usage:
1. Quinlan, J.R., "Learning with continuous classes", Proc. 5th Australian
Joint Conference on AI (eds A. Adams and L. Sterling), Singapore: World
Scientific, 1992
2. Quinlan, J.R., "Combining instance-based and model-based learning",
Proc. ML'93 (ed P.E. Utgoff), San Mateo: Morgan Kaufmann 1993
Results on 10-way cross-validation:
Method Average Relative
------ |Err| Error
------- --------
Guessing mean 1.15 1.00
Instance-based .52 .26
Regression .86 .49
Model trees .45 .29
Neural nets (G. Hinton) .30 .11
Regression+instances .48 .20
Model trees+instances .30 .17
NN+instances .29 .11
4. Relevant Information:
Ross Quinlan:
This data was given to me by Karl Ulrich at MIT in 1986. I didn't
record his description at the time, but here's his subsequent (1992)
recollection:
"I seem to remember that the data was from a simulation of a servo
system involving a servo amplifier, a motor, a lead screw/nut, and a
sliding carriage of some sort. It may have been on of the
translational axes of a robot on the 9th floor of the AI lab. In any
case, the output value is almost certainly a rise time, or the time
required for the system to respond to a step change in a position set
point."
(Quinlan, ML'93)
"This is an interesting collection of data provided by Karl
Ulrich. It covers an extremely non-linear phenomenon - predicting the
rise time of a servomechanism in terms of two (continuous) gain settings
and two (discrete) choices of mechanical linkages."
5. Number of Instances: 167
6. Number of Attributes: 4 + numeric class attribute
7. Attribute information:
1. motor: A,B,C,D,E
2. screw: A,B,C,D,E
3. pgain: 3,4,5,6
4. vgain: 1,2,3,4,5
5. class: 0.13 to 7.10
8. Missing Attribute Values: None