1993.bib

@COMMENT{{Automatically generated - DO NOT MODIFY!}}

@INPROCEEDINGS{Cunningham1993,
  AUTHOR = {Cunningham, S.J. and Denize, P.},
  TITLE = {A Tool for Model Generation and Knowledge Acquisition},
  BOOKTITLE = {Proc International Workshop on Artificial Intelligence and Statistics},
  PAGES = {213-222},
  ADDRESS = {Fort Lauderdale, Florida, USA},
  YEAR = {1993},
  ABSTRACT = {Tools to automatically induce domain descriptions from examples are valuable aids for the knowledge acquisition stage of expert system construction. This paper presents a description of an algorithm that induces two domain descriptions: a conceptual model, which gives a broad understanding of variable interactions and their effect on the system, and a predictive model, which determines the system value associated with variable values input to the model. This induction algorithm is based on Entropy Data Analysis (EDA), which builds linear equations to approximate the system described by the training data.},
  PDF = {http://www.cs.waikato.ac.nz/~ml/publications/1993/Cunningham-Denize93.pdf},
  PS = {http://www.cs.waikato.ac.nz/~ml/publications/1993/Cunningham-Denize93.ps}
}

@INPROCEEDINGS{Holmes1993,
  AUTHOR = {Holmes, G. and Cunningham, S.J.},
  TITLE = {Using Data Mining to Support the Construction and Maintenance of Expert Systems},
  BOOKTITLE = {Proc Artificial Neural Networks and Expert Systems},
  PAGES = {156-159},
  ADDRESS = {Dunedin, New Zealand},
  YEAR = {1993},
  ABSTRACT = {Many expert systems are constructed from and allied with a large collection of databases that are continually being updated. In this paper we address the issues of how such a knowledge base can be constructed using tools that search the databases for significant, unexpected correlations and present them to the knowledge engineer for review. Once a knowledge base has been constructed, there is the problem of keeping it consistent with changing conditions in the real world. Concepts which are embodied in the data may drift in time, and so some mechanism for updating the knowledge base must be developed. As it would prove costly to periodically revisit the knowledge acquisition phase, we propose to monitor the domain database automatically for "significant" concept change.},
  PDF = {http://www.cs.waikato.ac.nz/~ml/publications/1993/Holmes-Cunningham-DM93.pdf}
}

@INPROCEEDINGS{Holmes1993_2,
  AUTHOR = {Holmes, G. and Cunningham, S.J.},
  TITLE = {Expert Systems Development Using Data Mining},
  BOOKTITLE = {Proc Expert Systems '93},
  PAGES = {213-222},
  ADDRESS = {Cambridge, England},
  YEAR = {1993},
  ABSTRACT = {This paper presents a model of expert system construction beginning with an initial set of data chosen by a domain expert. The domain expert creates the knowledge base both through direct knowledge transfer (or through the construction of example sets for machine learning) and through mining one or more of the databases. As construction progresses, the domain expert may alter the composition of the databases by directing that additional or different data be collected. Interactively guided by the expert, the mining tool efficiently organises the search for findings in the database. These findings are then evaluated for inclusion in the knowledge base.},
  PDF = {http://www.cs.waikato.ac.nz/~ml/publications/1993/Holmes-Cunningham-ES93.pdf},
  PS = {http://www.cs.waikato.ac.nz/~ml/publications/1993/Holmes-Cunningham-ES93.ps}
}

@INPROCEEDINGS{Witten1993,
  AUTHOR = {Witten, I.H. and Cunningham, S. and Holmes, G. and McQueen, R.J. and Smith, L.A.},
  TITLE = {Practical Machine Learning and its Potential Application to Problems in Agriculture},
  BOOKTITLE = {Proc New Zealand Computer Conference},
  VOLUME = {1},
  PAGES = {308-325},
  ADDRESS = {Auckland, New Zealand},
  YEAR = {1993},
  ABSTRACT = {One of the most exciting and potentially far-reaching developments in contemporary computer science is the invention and application of methods of machine learning. These have evolved from simple adaptive parameter-estimation techniques to ways of (a) inducing classification rules from examples, (b) using prior knowledge to guide the interpretation of new examples, (c) using this interpretation to sharpen and refine the domain knowledge, and (d) storing and indexing example cases in ways that highlight their similarities and differences. Such techniques have been applied in domains ranging from the diagnosis of plant disease to the interpretation of medical test data. This paper reviews selected methods of machine learning with an emphasis on practical applications, and suggests how they might be used to address some important problems in the primary production industries, particularly agriculture.},
  PDF = {http://www.cs.waikato.ac.nz/~ml/publications/1993/Witten93-Practical-ML.pdf},
  PS = {http://www.cs.waikato.ac.nz/~ml/publications/1993/Witten93-Practical-ML.ps.gz}
}