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The Nature of Statistical Learning Theory
, 1999
"... Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based on the deve ..."
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Cited by 12976 (32 self)
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Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based
Incremental Learning from Positive Data
 Journal of Computer and System Sciences
, 1996
"... The present paper deals with a systematic study of incremental learning algorithms. ..."
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Cited by 36 (20 self)
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The present paper deals with a systematic study of incremental learning algorithms.
Iterative Learning from Positive Data and Negative Counterexamples
"... A model for learning in the limit is defined where a (socalled iterative) learner gets all positive examples from the target language, tests every new conjecture with a teacher (oracle) if it is a subset of the target language (and if it is not, then it receives a negative counterexample), and uses ..."
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A model for learning in the limit is defined where a (socalled iterative) learner gets all positive examples from the target language, tests every new conjecture with a teacher (oracle) if it is a subset of the target language (and if it is not, then it receives a negative counterexample
RHB+: A TypeOriented ILP System Learning from Positive Data
"... This paper presents the typeoriented relational learner RHB+. Attaching type information to hypotheses is effective in avoiding overgeneralization as well as enhancing readability and comprehensibility. In many areas, such as NLP, type information is actually available, while negative examples are ..."
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are not. Unfortunately, learning performance is usually poor if types are attached when only positive examples are available. RHB+ makes use of type information to efficiently compute informativity from positive examples only and to judge a stopping condition. The new technique of dynamic type restriction
Inverse entailment and Progol
, 1995
"... This paper firstly provides a reappraisal of the development of techniques for inverting deduction, secondly introduces ModeDirected Inverse Entailment (MDIE) as a generalisation and enhancement of previous approaches and thirdly describes an implementation of MDIE in the Progol system. Progol ..."
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Cited by 721 (61 self)
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is implemented in C and available by anonymous ftp. The reassessment of previous techniques in terms of inverse entailment leads to new results for learning from positive data and inverting implication between pairs of clauses.
Is learning by exporting important? Microdynamic evidence from Colombia, Mexico and Morocco
 Quarterly Journal of Economics
, 1998
"... Do firms become more efficient after becoming exporters? Do exporters generate positive externalities for domestically oriented producers? In this paper we tackle these questions by analyzing the causal links between exporting and productivity using plantlevel data. We look for evidence that firms ..."
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Cited by 592 (13 self)
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Do firms become more efficient after becoming exporters? Do exporters generate positive externalities for domestically oriented producers? In this paper we tackle these questions by analyzing the causal links between exporting and productivity using plantlevel data. We look for evidence that firms
Data Security
, 1979
"... The rising abuse of computers and increasing threat to personal privacy through data banks have stimulated much interest m the techmcal safeguards for data. There are four kinds of safeguards, each related to but distract from the others. Access controls regulate which users may enter the system and ..."
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Cited by 611 (3 self)
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The rising abuse of computers and increasing threat to personal privacy through data banks have stimulated much interest m the techmcal safeguards for data. There are four kinds of safeguards, each related to but distract from the others. Access controls regulate which users may enter the system
Learning the Kernel Matrix with SemiDefinite Programming
, 2002
"... Kernelbased learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information ..."
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Cited by 780 (22 self)
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problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques. When applied
Locally weighted learning
 ARTIFICIAL INTELLIGENCE REVIEW
, 1997
"... This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, ass ..."
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Cited by 594 (53 self)
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, assessing predictions, handling noisy data and outliers, improving the quality of predictions by tuning t parameters, interference between old and new data, implementing locally weighted learning e ciently, and applications of locally weighted learning. A companion paper surveys how locally weighted
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4,696,737