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Budgeted learning of naivebayes classifiers
 IN PROCEEDINGS OF 19TH CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE (UAI2003
, 2003
"... There is almost always a cost associated with acquiring training data. We consider the situation where the learner, with a fixed budget, may ‘purchase ’ data during training. In particular, we examine the case where observing the value of a feature of a training example has an associated cost, and t ..."
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Cited by 50 (4 self)
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There is almost always a cost associated with acquiring training data. We consider the situation where the learner, with a fixed budget, may ‘purchase ’ data during training. In particular, we examine the case where observing the value of a feature of a training example has an associated cost, and the total cost of all feature values acquired during training must remain less than this fixed budget. This paper compares methods for sequentially choosing which feature value to purchase next, given the budget and user’s current knowledge of Naïve Bayes model parameters. Whereas active learning has traditionally focused on myopic (greedy) approaches and uniform/roundrobin policies for query selection, this paper shows that such methods are often suboptimal and presents a tractable method for incorporating knowledge of the budget in the information acquisition process.
Learning and Classifying under Hard Budgets
 In Proceedings of the European Conference on Machine Learning (ECML05
, 2005
"... Abstract. Since resources for data acquisition are seldom infinite, both learners and classifiers must act intelligently under hard budgets. In this paper, we consider problems in which feature values are unknown to both the learner and classifier, but can be acquired at a cost. Our goal is a learne ..."
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Cited by 43 (3 self)
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Abstract. Since resources for data acquisition are seldom infinite, both learners and classifiers must act intelligently under hard budgets. In this paper, we consider problems in which feature values are unknown to both the learner and classifier, but can be acquired at a cost. Our goal is a learner that spends its fixed learning budget bL acquiring training data, to produce the most accurate “active classifier ” that spends at most bC per instance. To produce this fixedbudget classifier, the fixedbudget learner must sequentially decide which feature values to collect to learn the relevant information about the distribution. We explore several approaches the learner can take, including the standard “round robin” policy (purchasing every feature of every instance until the bL budget is exhausted). We demonstrate empirically that round robin is problematic (especially for small bL), and provide alternate learning strategies that achieve superior performance on a variety of datasets. 1
Budgeted learning of bounded active classifiers
 In Proceedings of the ACM SIGKDD Workshop on UtilityBased Data Mining
, 2005
"... Abstract. Since resources for data acquisition are seldom infinite, the need exists for learners and classifiers that act intelligently under hard budgets. In this paper, we consider problems in which feature values are unknown to the learner and classifier, but can be acquired at a cost. The goal i ..."
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Cited by 2 (1 self)
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Abstract. Since resources for data acquisition are seldom infinite, the need exists for learners and classifiers that act intelligently under hard budgets. In this paper, we consider problems in which feature values are unknown to the learner and classifier, but can be acquired at a cost. The goal is a learner that spends its learning budget bL acquiring training data so as to produce the most accurate active classifier that spends at most bC per instance. From the learner’s perspective, purchasing every feature of every instance is sure to approach the underlying distribution asymptotically, but will this yield the best distribution when only bL dollars worth of data can be collected? In this work, we show empirically that the answer is no (especially for small bL) and present alternate learning strategies that achieve superior performance on a variety of realworld datasets. 1
Budgeted Learning, Part I: The MultiArmed Bandit Case
, 2003
"... We introduce and motivate the task of learning under a budget. We focus on a basic problem in this space: selecting the optimal bandit after a period of experimentation in a multiarmed bandit setting, where each experiment is costly, our total costs cannot exceed a fixed prespecified budget, ..."
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Cited by 2 (2 self)
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We introduce and motivate the task of learning under a budget. We focus on a basic problem in this space: selecting the optimal bandit after a period of experimentation in a multiarmed bandit setting, where each experiment is costly, our total costs cannot exceed a fixed prespecified budget, and there is no reward collection during the learning period. We address the computational complexity of the problem, propose a number of algorithms, and report on the performance of the algorithms, including their (worstcase) approximation properties, as well as their empirical performance on various different problem instances. Our
Massive Online Teaching to Bounded Learners ABSTRACT
"... We consider a model of teaching in which the learners are consistent and have bounded state, but are otherwise arbitrary. The teacher is noninteractive and “massively open”: the teacher broadcasts a sequence of examples of an arbitrary target concept, intended for every possible online learning al ..."
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We consider a model of teaching in which the learners are consistent and have bounded state, but are otherwise arbitrary. The teacher is noninteractive and “massively open”: the teacher broadcasts a sequence of examples of an arbitrary target concept, intended for every possible online learning algorithm to learn from. We focus on the problem of designing interesting teachers: efficient sequences of examples that lead all capable and consistent learners to learn concepts, regardless of the underlying algorithm used by the learner. We use two measures of teaching efficiency: the number of mistakes made by the worstcase learner, and the maximum length of the example sequence needed for the worstcase learner. Our results are summarized as follows: • Given a uniform random sequence of examples of an nbit concept function, learners (capable of consistently learning the concept) with s(n) bits of state are guaranteed to make only O(n · s(n)) mistakes and exactly learn the concept, with high probability. This theorem has interesting corollaries; for instance, every concept c has a sequence of examples can teach c to all capable consistent online learners implementable with s(n)size circuits, such that every learner makes only Õ(s(n)2) mistakes. That is, all resourcebounded algorithms capable of consistently learning a concept can be simultaneously taught that concept with few mistakes, on a single example sequence. We also show how to efficiently generate such a sequence of examples online: using Nisan’s pseudorandom generator, each example in the sequence can be generated with polynomialtime overhead per example, with an O(n · s(n))bit initial seed.
Harvard and MIT
, 2012
"... We consider a model of teaching in which the learners are consistent and have bounded state, but are otherwise arbitrary. The teacher is noninteractive and “massively open”: the teacher broadcasts a sequence of examples of an arbitrary target concept, intended for every possible online learning al ..."
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We consider a model of teaching in which the learners are consistent and have bounded state, but are otherwise arbitrary. The teacher is noninteractive and “massively open”: the teacher broadcasts a sequence of examples of an arbitrary target concept, intended for every possible online learning algorithm to learn from. We focus on the problem of designing interesting teachers: sequences of examples that allow all capable and consistent learners to efficiently learn concepts, regardless of the underlying algorithm used by the learner. We use two measures of efficiency: the number of mistakes made by the worstcase learner, and the maximum length of the example sequence needed for the worstcase learner. Our results are summarized as follows: • Given a uniform random sequence of examples of an nbit concept function, learners (capable of consistently learning the concept) with s(n) bits of state are guaranteed to make only O(n · s(n)) mistakes and exactly learn the concept, with high probability. This theorem has interesting corollaries; for instance, every concept c has a sequence of examples can teach c to all capable consistent online learners implementable with s(n)size circuits, such that every learner makes only Õ(s(n)2) mistakes. That is, all resourcebounded algorithms capable of consistently learning a
Supervisor
"... Permission is hereby granted to the University of Alberta Library to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. The author reserves all other publication and other rights in association with the copyright in the ..."
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Permission is hereby granted to the University of Alberta Library to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. The author reserves all other publication and other rights in association with the copyright in the thesis, and except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatever without the author’s prior written permission.
Budgeted Learning, Part II: The NaïveBayes Case
"... There is almost always a cost associated with acquiring training data. We consider the situation where the learner, with a xed budget, may `purchase' data during training. In particular, we examine the case where each feature value has an associated cost, and the total cost of all featur ..."
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There is almost always a cost associated with acquiring training data. We consider the situation where the learner, with a xed budget, may `purchase' data during training. In particular, we examine the case where each feature value has an associated cost, and the total cost of all feature values acquired during training must remain less than this xed budget. This paper compares methods for choosing which feature value to purchase next, given the budget and user's current knowledge of the Nave Bayes parameters.