Toward memory-based reasoning

Stanfill, Craig; Waltz, David L.

doi:10.1145/7902.7906

Cited by 963 publications

(400 citation statements)

References 21 publications

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“…A more sophisticated alternative consists of considering two symbolic values to be similar if they make similar predictions (i.e., if they correlate similarly with the class feature). This was first proposed by Stanfill and Waltz (1986) as part of their value difference metric (VDM) for a memory-based reasoner. Here we will consider a simplified version of the VDM, which defines the distance between two symbolic values as:…”

Section: Instance-based Learningmentioning

confidence: 99%

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Unifying instance-based and rule-based induction

1996

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Abstract. Several well-developed approaches to inductive learning now exist, but each has specific limitations that are hard to overcome. Multi-strategy learning attempts to tackle this problem by combining multiple methods in one algorithm. This article describes a unification of two widely-used empirical approaches: rule induction and instance-based learning. In the new algorithm, instances are treated as maximally specific rules, and classification is performed using a best-match strategy. Rules are learned by gradually generalizing instances until no improvement in apparent accuracy is obtained. Theoretical analysis shows this approach to be efficient. It is implemented in the RISE 3.1 system. In an extensive empirical study, RISE consistently achieves higher accuracies than state-ofthe-art representatives of both its parent approaches (PEBLS and CN2), as well as a decision tree learner (C4.5). Lesion studies show that each of RISE's components is essential to this performance. Most significantly, in 14 of the 30 domains studied, RISE is more accurate than the best of PEBLS and CN2, showing that a significant synergy can be obtained by combining multiple empirical methods.

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Section: Instance-based Learningmentioning

confidence: 99%

“…The total distance ∆(E 1 , E 2 ) is computed as before. Different variants of this metric have been successfully used in pronunciation, molecular biology and other tasks (Stanfill & Waltz, 1986;Cost & Salzberg, 1993;Biberman, 1994). IBL methods need to address the problem of sensitivity to irrelevant attributes.…”

Section: Instance-based Learningmentioning

confidence: 99%

Unifying instance-based and rule-based induction

1996

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“…Obviously the Manhattan distance function is only appropriate for continuous or scaled parameters p. For discrete parameters we implemented the Value Difference Metric (VDM) as proposed in [15] and improved in [16]. Given two findings f 1 = (p = x) and f 2 = (p = y) the VDM defines the distance between the two values x and y of parameter p:…”

Section: Similarity Knowledge For Parameter Valuesmentioning

confidence: 99%

Inductive Learning for Case-Based Diagnosis with Multiple Faults

Baumeister

Atzmüller

Puppe

2002

Lecture Notes in Computer Science

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Abstract. We present adapted inductive methods for learning similarities, parameter weights and diagnostic profiles for case-based reasoning. All of these methods can be refined incrementally by applying different types of background knowledge. Diagnostic profiles are used for extending the conventional CBR to solve cases with multiple faults. The context of our work is to supplement a medical documentation and consultation system by CBR techniques, and we present an evaluation with a real-world case base.

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“…These domains have received considerable attention from connectionist researchers who employed the back propagation learning algorithm (Sejnowski & Rosenberg, 1986;Qian & Sejnowski, 1988;Towell et al, 1990). In addition, the word pronunciation problem has been the subject of a number of comparisons using other machine learning algorithms (Stanfill & Waltz, 1986;Shavlik et al, 1989;Dietterich et al, 1990). All of these domains represent problems of considerable practical importance, and all have symbolic feature values, which makes them difficult for conventional nearest neighbor algorithms.…”

Section: Instance-based Learning Versus Other Modelsmentioning

confidence: 99%

“…Our algorithm constructs modified "value difference" tables (in the style of Stanfill & Waltz (1986)) to produce a non-Euclidean distance metric, and we introduce the idea of "exception spaces" that result when weights are attached to individual examples. The combination of these two techniques results in a robust instance-based learning algorithm that works for any domain with symbolic feature values.…”

Section: Introductionmentioning

confidence: 99%

A weighted nearest neighbor algorithm for learning with symbolic features

1993

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Abstract. In the past, nearest neighbor algorithms for learning from examples have worked best in domains in which all features had numeric values. In such domains, the examples can be treated as points and distance metrics can use standard defioJtions. In symbolic domains, a more sophisticated treatment of the feature space is required. We introduce a nearest neighbor algorithm for learning in domains with symbolic features. Our algorithm calculates distance tables that allow it to produce real-valued distances between instances, and attaches weights to the instances to further modify the structure of feature space. We show that this technique produces excellent classification accuracy on three problems that have been studied by machine learning researchers: predicting protein secondary structure, identifying DNA promoter sequences, and pronouncing English text. Direct experimental comparisons with the other learning algorithms show that our nearest neighbor algorithm is comparable or superior in all three domains. In addition, our algorithm has advantages in training speed, simplicity, and perspicuity. We conclude that experimental evidence favors the use and continued development of nearest neighbor algorithms for domains such as the ones studied here.

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Toward memory-based reasoning

Cited by 963 publications

References 21 publications

Unifying instance-based and rule-based induction

Unifying instance-based and rule-based induction

Inductive Learning for Case-Based Diagnosis with Multiple Faults

A weighted nearest neighbor algorithm for learning with symbolic features

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