Hayes-Roth Data Set
Below are papers that cite this data set, with context shown.
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Yuan Jiang and Zhi-Hua Zhou. Editing Training Data for kNN Classifiers with Neural Network Ensemble. ISNN (1). 2004.
i.e. annealing, credit, liver, pima, soybean, wine and zoo. RemoveOnly obtains the best performance on three data sets, i.e. glass, hayes roth and wine. It is surprising that Depuration obtains the best performance on only one data set, i.e. iris, as RelabelOnly does. These observations indicate that NNEE is a
Bob Ricks and Dan Ventura. Training a Quantum Neural Network. NIPS. 2003.
an epoch refers to finding and fixing the weight of a single node. We also tried the randomized search algorithm for a few real-world machine learning problems: lenses, Hayes Roth and the iris datasets . The lenses data set is a data set that tries to predict whether people will need soft contact lenses, hard contact lenses or no contacts. The iris dataset details features of three different
Gabor Melli. A Lazy Model-Based Approach to On-Line Classification. University of British Columbia. 1989.
The five selected datasets were: echocardiogram, hayes roth heart, horse-colic,andiris datasets. These datasets (marked in Table 7.1 with a * symbol beside their name) contain a sampling of attribute types and domains. For
Anthony D. Griffiths and Derek Bridge. A Yardstick for the Evaluation of Case-Based Classifiers. Department of Computer Science, University of York.
used in Figures 5 and 4 The data set in the UCI repository for the Hayes Roth target function described above is incomplete in that it contains instances for only some of the possible descriptions, contains duplications, and contains
Jerome H. Friedman and Ron Kohavi and Youngkeol Yun. To appear in AAAI-96 Lazy Decision Trees. Statistics Department and Stanford Linear Accelerator Center Stanford University.
monk1, monk2, and monk3; and pseudo-artificial datasets: tic-tac-toe, and chess. Hayes roth and glass2 also have large differences probably because they have many strongly relevant features and few weakly relevant features (John, Kohavi, & Pfleger