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Types of cost in inductive concept learning
 In Workshop on CostSensitive Learning at the Seventeenth International Conference on Machine Learning
, 2000
"... Inductive concept learning is the task of learning to assign cases to a discrete set of classes. In realworld applications of concept learning, there are many different types of cost involved. The majority of the machine learning literature ignores all types of cost (unless accuracy is interpreted ..."
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Cited by 132 (0 self)
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Inductive concept learning is the task of learning to assign cases to a discrete set of classes. In realworld applications of concept learning, there are many different types of cost involved. The majority of the machine learning literature ignores all types of cost (unless accuracy is interpreted
The CN2 Induction Algorithm
 MACHINE LEARNING
, 1989
"... Systems for inducing concept descriptions from examples are valuable tools for assisting in the task of knowledge acquisition for expert systems. This paper presents a description and empirical evaluation of a new induction system, cn2, designed for the efficient induction of simple, comprehensib ..."
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Cited by 890 (6 self)
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Systems for inducing concept descriptions from examples are valuable tools for assisting in the task of knowledge acquisition for expert systems. This paper presents a description and empirical evaluation of a new induction system, cn2, designed for the efficient induction of simple
Strongly Typed Inductive Concept Learning
 Proceedings of the 8th International Conference on Inductive Logic Programming, volume 1446 of Lecture Notes in Artificial Intelligence
, 1998
"... . In this paper we argue that the use of a language with a type system, together with higherorder facilities and functions, provides a suitable basis for knowledge representation in inductive concept learning and, in particular, illuminates the relationship between attributevalue learning and indu ..."
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Cited by 31 (15 self)
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. In this paper we argue that the use of a language with a type system, together with higherorder facilities and functions, provides a suitable basis for knowledge representation in inductive concept learning and, in particular, illuminates the relationship between attributevalue learning
Consumers and Their Brands: Developing Relationship Theory
 Journal of consumer research
, 1998
"... Although the relationship metaphor dominates contemporary marketing thought and practice, surprisingly little empirical work has been conducted on relational phenomena in the consumer products domain, particularly at the level of the brand. In this article, the author: (1) argues for the validity of ..."
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Cited by 552 (3 self)
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of relationships consumers form with brands; and (3) inducts from the data the concept of brand relationship quality, a diagnostic tool for conceptualizing and evaluating relationship strength. Three indepth case studies inform this agenda, their interpretation guided by an integrative review of the literature
Irrelevant Features and the Subset Selection Problem
 MACHINE LEARNING: PROCEEDINGS OF THE ELEVENTH INTERNATIONAL
, 1994
"... We address the problem of finding a subset of features that allows a supervised induction algorithm to induce small highaccuracy concepts. We examine notions of relevance and irrelevance, and show that the definitions used in the machine learning literature do not adequately partition the features ..."
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Cited by 757 (26 self)
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We address the problem of finding a subset of features that allows a supervised induction algorithm to induce small highaccuracy concepts. We examine notions of relevance and irrelevance, and show that the definitions used in the machine learning literature do not adequately partition the features
The ordinal numbers
 Journal of Formalized Mathematics
, 1989
"... Summary. We present the choice function rule in the beginning of the article. In the main part of the article we formalize the base of cardinal theory. In the first section we introduce the concept of cardinal numbers and order relations between them. We present here CantorBernstein theorem and oth ..."
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Cited by 731 (68 self)
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and other properties of order relation of cardinals. In the second section we show that every set has cardinal number equipotence to it. We introduce notion of alephs and we deal with the concept of finite set. At the end of the article we show two schemes of cardinal induction. Some definitions are based
Solving multiclass learning problems via errorcorrecting output codes
 JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH
, 1995
"... Multiclass learning problems involve nding a de nition for an unknown function f(x) whose range is a discrete set containing k>2values (i.e., k \classes"). The de nition is acquired by studying collections of training examples of the form hx i;f(x i)i. Existing approaches to multiclass l ..."
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Cited by 726 (8 self)
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learning problems include direct application of multiclass algorithms such as the decisiontree algorithms C4.5 and CART, application of binary concept learning algorithms to learn individual binary functions for each of the k classes, and application of binary concept learning algorithms with distributed
Feature Discovery for Inductive Concept Learning
, 1993
"... This paper describes Zenith, a discovery system that performs constructive induction. The system is able to generate and extend new features for concept learning using agendabased heuristic search. The search is guided by feature worth (a composite measure of discriminability and cost). Zenith is d ..."
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Cited by 8 (0 self)
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This paper describes Zenith, a discovery system that performs constructive induction. The system is able to generate and extend new features for concept learning using agendabased heuristic search. The search is guided by feature worth (a composite measure of discriminability and cost). Zenith
An analysis of Bayesian classifiers
 IN PROCEEDINGS OF THE TENTH NATIONAL CONFERENCE ON ARTI CIAL INTELLIGENCE
, 1992
"... In this paper we present anaveragecase analysis of the Bayesian classifier, a simple induction algorithm that fares remarkably well on many learning tasks. Our analysis assumes a monotone conjunctive target concept, and independent, noisefree Boolean attributes. We calculate the probability that t ..."
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Cited by 440 (17 self)
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In this paper we present anaveragecase analysis of the Bayesian classifier, a simple induction algorithm that fares remarkably well on many learning tasks. Our analysis assumes a monotone conjunctive target concept, and independent, noisefree Boolean attributes. We calculate the probability
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