Pattern Recognition as Rule-Guided Inductive Inference

Michalski, Ryszard S.

doi:10.1109/tpami.1980.4767034

Cited by 341 publications

(136 citation statements)

References 12 publications

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“…In a paper introducing his INDUC~ system, Michalski (1980) describes an artificial task of learning to predict whether a train is headed east or west. This task illustrates the kind of structured objects with varying numbers of substructures that cause problems for attributevalue representation; the ten trains of Figure 4 have different numbers of cars and some cars carry more than one load.…”

Section: Learning Where Trains Are Headingmentioning

confidence: 99%

Learning logical definitions from relations

1990

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Section: Learning Where Trains Are Headingmentioning

confidence: 99%

Learning logical definitions from relations

1990

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“…AQ-15c represents training examples using a restricted version of the attributional language VL 1 (Michalski, 1980). Rule conditions are of the form…”

Section: Description Of the Aq-pm Learning Systemmentioning

confidence: 99%

“…To find extreme or boundary training examples, AQ-PM uses characteristic decision rules, which specify the common attributes of domain objects from the same class (Michalski, 1980). These rules consist of all the domain attributes and their values for the objects represented in the training set, and form the tightest possible hyper-rectangle around a cluster of examples.…”

Section: Selecting Examplesmentioning

confidence: 99%

“…We have investigated this notion using two incremental algorithms: the GEM algorithm (Reinke & Michalski, 1988), a full instance memory technique, and the AQ-11 algorithm (Michalski & Larson, 1983), a no instance memory technique. We chose these algorithms because their inductive biases are most similar to that of AQ-PM: both use the VL 1 representation language (Michalski, 1980) and the AQ induction algorithm (Michalski, 1969).…”

Section: Incremental Learningmentioning

confidence: 99%

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2000

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Abstract. This paper describes a method for selecting training examples for a partial memory learning system. The method selects extreme examples that lie at the boundaries of concept descriptions and uses these examples with new training examples to induce new concept descriptions. Forgetting mechanisms also may be active to remove examples from partial memory that are irrelevant or outdated for the learning task. Using an implementation of the method, we conducted a lesion study and a direct comparison to examine the effects of partial memory learning on predictive accuracy and on the number of training examples maintained during learning. These experiments involved the STAGGER Concepts, a synthetic problem, and two real-world problems: a blasting cap detection problem and a computer intrusion detection problem. Experimental results suggest that the partial memory learner notably reduced memory requirements at the slight expense of predictive accuracy, and tracked concept drift as well as other learners designed for this task.

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“…Most versions form the star by specialising from candidate rules formed from single attributes (see for example Michalski, 1980.) By contrast, one version, INDUCE1.2 (Dietterich and Michalski, 1981), forms the star by generalising from a candidate rule formed directly from the instance being examined.…”

Section: Introductionmentioning

confidence: 99%

Techniques for efficient empirical induction

Webb

1990

Ai '88

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This paper describes the LEI algorithm for empirical induction. The LEI algorithm provides efficient empirical induction for discrete attribute value data. It derives a classification procedure in the form of a set of predicate logic classification rules. This contrasts with the only other efficient approach to exhaustive empirical induction, the derivatives of the CLS algorithm, which present their classification procedures in the form of a decision tree. The LEI algorithm will always find the simplest nondisjunctive rule that correctly classifies all examples of a single class where such a rule exists.

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Pattern Recognition as Rule-Guided Inductive Inference

Cited by 341 publications

References 12 publications

Learning logical definitions from relations

Learning logical definitions from relations

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Techniques for efficient empirical induction

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