Book Chapter10.1016/B978-1-55860-036-2.50118-1
Evaluating alternative instance representations
Sharad Saxena
- 01 Dec 1989
- pp 465-468
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TL;DR: An algorithm for estimating the complexity of the function from a set of examples is proposed and was able to correctly identify the better of the two given representations for the two-or-more-clumps problem.
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Abstract: This paper addresses the problem of evaluating which, among a given set of alternative representations of a problem, is best suited for learning from examples. It is argued that the representation that leads to a simpler function of the input features is best suited for learning. An algorithm for estimating the complexity of the function from a set of examples is proposed. The algorithm was able to correctly identify the better of the two given representations for the two-or-more-clumps problem.
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Citations
Very Simple Classification Rules Perform Well on Most Commonly Used Datasets
TL;DR: On most datasets studied, the best of very simple rules that classify examples on the basis of a single attribute is as accurate as the rules induced by the majority of machine learning systems.
Selective reformulation of examples in concept learning
Jean-Daniel Zucker,Jean-Gabriel Ganascia +1 more
- 10 Jul 1994
TL;DR: This paper describes a novel approach to perform representation shifts on learning examples using the notion of morion (from the Greek) to qualify this structure and describes an algorithm which reformulates learning examples automatically and goes on to analyze its complexity.
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Feature Discovery for Inductive Concept Learning
Tom E. Fawcett
- 01 Feb 1990
TL;DR: Zenith is a discovery system that performs constructive induction that is able to generate and extend new features for concept learning using agenda-based heuris- tic search and its ability to extend its knowledge base by creating new domain classes is distinguished.
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Knowledge-based feature generation
James P. Callan
- 01 Dec 1989
TL;DR: A method of automatically enriching an instance description language with new, problem-specific terms is described, independent of any particular learning algorithm or concept description language, guided by whatever domain-specific information is available, such as a search problem specification.
8
Use of Domain Knowledge in Constructive Induction
Jamie Callan
- 01 Jan 1990
TL;DR: This thesis proposal considers the representation problem for a class of architectures in which the problem-solver is a search procedure and the inductive learning algorithm is a source of heuristic guidance and presents a solution that results in faster and more accurate learning.
4
References
Minimization of Boolean functions
TL;DR: A systematic procedure is presented for writing a Boolean function as a minimum sum of products and specific attention is given to terms which can be included in the function solely for the designer's convenience.
1.2K
The Problem of Simplifying Truth Functions
TL;DR: The Problem of Simplifying Truth Functions is concerned with the problem of reducing the number of operations on a graph to a simple number.
963
On the connection between the complexity and credibility of inferred models
TL;DR: This paper derives formal relationships between n, c and the probability of ambiguous predictions by examining three modeling languages under binary classification tasks: perceptrons, Boolean formulae, and Boolean networks.