Data Set Information:

The estimated relative performance values were estimated by the authors using a linear regression method.  See their article (pp 308-313) for more details on how the relative performance values were set.

Attribute Information:

1. vendor name: 30
     (adviser, amdahl,apollo, basf, bti, burroughs, c.r.d, cambex, cdc, dec,
      dg, formation, four-phase, gould, honeywell, hp, ibm, ipl, magnuson,
      microdata, nas, ncr, nixdorf, perkin-elmer, prime, siemens, sperry,
      sratus, wang)
  2. Model Name: many unique symbols
  3. MYCT: machine cycle time in nanoseconds (integer)
  4. MMIN: minimum main memory in kilobytes (integer)
  5. MMAX: maximum main memory in kilobytes (integer)
  6. CACH: cache memory in kilobytes (integer)
  7. CHMIN: minimum channels in units (integer)
  8. CHMAX: maximum channels in units (integer)
  9. PRP: published relative performance (integer)
 10. ERP: estimated relative performance from the original article (integer)

Relevant Papers:

Ein-Dor and Feldmesser (CACM 4/87, pp 308-317)

Kibler,D. & Aha,D. (1988).  Instance-based Prediction of Real-Valued Attributes.  In Proceedings of the CSCSI (Canadian AI) Conference.
[Web link]

Papers That Cite This Data Set¹:

Dan Pelleg. Scalable and Practical Probability Density Estimators for Scientific Anomaly Detection. School of Computer Science Carnegie Mellon University. 2004.  [View Context].

Yongge Wang. A New Approach to Fitting Linear Models in High Dimensional Spaces. Alastair Scott (Department of Statistics, University of Auckland).  [View Context].

Citation Request:

Please refer to the Machine Learning Repository's citation policy

Creator:

Phillip Ein-Dor and Jacob Feldmesser
Ein-Dor: Faculty of Management
Tel Aviv University; Ramat-Aviv;
Tel Aviv, 69978; Israel

Donor:

David W. Aha (aha '@' ics.uci.edu) (714) 856-8779