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Query Learning with Large Margin Classifiers
, 2000
"... The active selection of instances can significantly improve the generalisation performance of a learning machine. Large margin classifiers such as Support Vector Machines classify data using the most informative instances (the support vectors). This makes them natural candidates for instance s ..."
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Cited by 157 (1 self)
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The active selection of instances can significantly improve the generalisation performance of a learning machine. Large margin classifiers such as Support Vector Machines classify data using the most informative instances (the support vectors). This makes them natural candidates for instance
Preference Elicitation and Query Learning
 Journal of Machine Learning Research
, 2004
"... In this paper we explore the relationship between "preference elicitation", a learningstyle problem that arises in combinatorial auctions, and the problem of learning via queries studied in computational learning theory. Preference elicitation is the process of asking questions about th ..."
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Cited by 39 (7 self)
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In this paper we explore the relationship between "preference elicitation", a learningstyle problem that arises in combinatorial auctions, and the problem of learning via queries studied in computational learning theory. Preference elicitation is the process of asking questions about
Query Learning and Certificates in Lattices
"... Abstract. We provide an abstract version, in terms of lattices, of the Horn query learning algorithm of Angluin, Frazier, and Pitt. To validate it, we develop a proof that is independent of the propositional Horn logic structure. We also construct a certificate set for the class of lattices that gen ..."
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Cited by 2 (2 self)
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Abstract. We provide an abstract version, in terms of lattices, of the Horn query learning algorithm of Angluin, Frazier, and Pitt. To validate it, we develop a proof that is independent of the propositional Horn logic structure. We also construct a certificate set for the class of lattices
Query learning with exponential query costs
, 2010
"... In query learning, the goal is to identify an unknown object while minimizing the number of “yes ” or “no ” questions (queries) posed about that object. A wellstudied algorithm for query learning is known as generalized binary search (GBS). We show that GBS is a greedy algorithm to optimize the exp ..."
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Cited by 1 (1 self)
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In query learning, the goal is to identify an unknown object while minimizing the number of “yes ” or “no ” questions (queries) posed about that object. A wellstudied algorithm for query learning is known as generalized binary search (GBS). We show that GBS is a greedy algorithm to optimize
Query by Committee
, 1992
"... We propose an algorithm called query by committee, in which a committee of students is trained on the same data set. The next query is chosen according to the principle of maximal disagreement. The algorithm is studied for two toy models: the highlow game and perceptron learning of another perceptr ..."
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Cited by 432 (3 self)
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We propose an algorithm called query by committee, in which a committee of students is trained on the same data set. The next query is chosen according to the principle of maximal disagreement. The algorithm is studied for two toy models: the highlow game and perceptron learning of another
Materializing and Querying Learned Knowledge
"... Abstract. In many Semantic Web domains a tremendous number of statements (expressed as triples) can potentially be true but, in a given domain, only a small number of statements is known to be true or can be inferred to be true. It thus makes sense to attempt to estimate the truth values of statemen ..."
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Cited by 23 (17 self)
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of statements by exploring regularities in the Semantic Web data via machine learning. Our goal is a “pushbutton ” learning approach that requires a minimum of user intervention. The learned knowledge is materialized offline (at loading time) such that querying is fast. We define an extension of SPARQL
Selective sampling using the Query by Committee algorithm
 Machine Learning
, 1997
"... We analyze the "query by committee" algorithm, a method for filtering informative queries from a random stream of inputs. We show that if the twomember committee algorithm achieves information gain with positive lower bound, then the prediction error decreases exponentially with the numbe ..."
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Cited by 433 (7 self)
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with the number of queries. We show that, in particular, this exponential decrease holds for query learning of perceptrons.
The subsumption lattice and query learning
 Proceedings of the International Conference on Algorithmic Learning Theory
, 2004
"... The paper identifies several new properties of the lattice induced by the subsumption relation over firstorder clauses and derives implications of these for learnability. In particular, it is shown that the length of subsumption chains of function free clauses with bounded size can be exponential i ..."
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Cited by 5 (3 self)
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which are not related to each other by subsumption. This is used to show that recent pairingbased algorithms can make exponentially many queries on some learning problems. 1
Improving generalization with active learning
 Machine Learning
, 1994
"... Abstract. Active learning differs from "learning from examples " in that the learning algorithm assumes at least some control over what part of the input domain it receives information about. In some situations, active learning is provably more powerful than learning from examples ..."
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Cited by 544 (1 self)
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Abstract. Active learning differs from "learning from examples " in that the learning algorithm assumes at least some control over what part of the input domain it receives information about. In some situations, active learning is provably more powerful than learning from examples
Canonical Horn Representations and Query Learning
"... We describe an alternative construction of an existing canonical representation for definite Horn theories, the GuiguesDuquenne basis (or GD basis), which minimizes a natural notion of implicational size. We extend the canonical representation to general Horn, by providing a reduction from definite ..."
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Cited by 1 (1 self)
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definite to general Horn CNF. We show how this representation relates to two topics in query learning theory: first, we show that a wellknown algorithm by Angluin, Frazier and Pitt that learns Horn CNF always outputs the GD basis independently of the counterexamples it receives; second, we build strong
Results 1  10
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