Corpus ID: 58809019

Machine Learning and Data Mining; Methods and Applications

@inproceedings{Michalski1998MachineLA,
  title={Machine Learning and Data Mining; Methods and Applications},
  author={R. Michalski and I. Bratko and Avan Bratko},
  year={1998}
}
From the Publisher: Master the new computational tools to get the most out of your information system. This practical guide, the first to clearly outline the situation for the benefit of engineers and scientists, provides a straightforward introduction to basic machine learning and data mining methods, covering the analysis of numerical, text, and sound data. 

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References

SHOWING 1-5 OF 5 REFERENCES
Comparing International Development Patterns Using Multi-Operator Learning and Discovery Tools
TLDR
Preliminary experiments focus on discerning and comparing various patterns in the status and development of countries in different regions of the world. Expand
Selection of Most Representative Training Examples and Incremental Generation of VL1 Hypotheses: The Underlying Methodology and the Description of Programs ESEL and AQ11
This work was supported in part by a National Science Foundation Grant NSF MCS 76-22940 and in part by a Senior Visiting Fellowship from British Science Research Council.
Rough Sets: Theoretical Aspects of Reasoning about Data
I. Theoretical Foundations.- 1. Knowledge.- 1.1. Introduction.- 1.2. Knowledge and Classification.- 1.3. Knowledge Base.- 1.4. Equivalence, Generalization and Specialization of Knowledge.- Summary.-Expand
A Planar Geometrical Model for Representing Multi-Dimensional Discrete Spaces and Multiple-Valued Logic Functions
This work was supported in part by the National Science Foundation under grant NSF MCS 76-22940.