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353
Combining labeled and unlabeled data with cotraining
, 1998
"... We consider the problem of using a large unlabeled sample to boost performance of a learning algorithm when only a small set of labeled examples is available. In particular, we consider a setting in which the description of each example can be partitioned into two distinct views, motivated by the ta ..."
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Cited by 1633 (28 self)
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We consider the problem of using a large unlabeled sample to boost performance of a learning algorithm when only a small set of labeled examples is available. In particular, we consider a setting in which the description of each example can be partitioned into two distinct views, motivated by the task of learning to classify web pages. For example, the description of a web page can be partitioned into the words occurring on that page, and the words occurring in hyperlinks that point to that page. We assume that either view of the example would be su cient for learning if we had enough labeled data, but our goal is to use both views together to allow inexpensive unlabeled data to augment amuch smaller set of labeled examples. Speci cally, the presence of two distinct views of each example suggests strategies in which two learning algorithms are trained separately on each view, and then each algorithm's predictions on new unlabeled examples are used to enlarge the training set of the other. Our goal in this paper is to provide a PACstyle analysis for this setting, and, more broadly, a PACstyle framework for the general problem of learning from both labeled and unlabeled data. We also provide empirical results on real webpage data indicating that this use of unlabeled examples can lead to signi cant improvement of hypotheses in practice. As part of our analysis, we provide new re
Boosting a Weak Learning Algorithm By Majority
, 1995
"... We present an algorithm for improving the accuracy of algorithms for learning binary concepts. The improvement is achieved by combining a large number of hypotheses, each of which is generated by training the given learning algorithm on a different set of examples. Our algorithm is based on ideas pr ..."
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Cited by 516 (16 self)
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We present an algorithm for improving the accuracy of algorithms for learning binary concepts. The improvement is achieved by combining a large number of hypotheses, each of which is generated by training the given learning algorithm on a different set of examples. Our algorithm is based on ideas presented by Schapire in his paper "The strength of weak learnability", and represents an improvement over his results. The analysis of our algorithm provides general upper bounds on the resources required for learning in Valiant's polynomial PAC learning framework, which are the best general upper bounds known today. We show that the number of hypotheses that are combined by our algorithm is the smallest number possible. Other outcomes of our analysis are results regarding the representational power of threshold circuits, the relation between learnability and compression, and a method for parallelizing PAC learning algorithms. We provide extensions of our algorithms to cases in which the conc...
Correlation Clustering
 MACHINE LEARNING
, 2002
"... We consider the following clustering problem: we have a complete graph on # vertices (items), where each edge ### ## is labeled either # or depending on whether # and # have been deemed to be similar or different. The goal is to produce a partition of the vertices (a clustering) that agrees as mu ..."
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Cited by 332 (4 self)
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We consider the following clustering problem: we have a complete graph on # vertices (items), where each edge ### ## is labeled either # or depending on whether # and # have been deemed to be similar or different. The goal is to produce a partition of the vertices (a clustering) that agrees as much as possible with the edge labels. That is, we want a clustering that maximizes the number of # edges within clusters, plus the number of edges between clusters (equivalently, minimizes the number of disagreements: the number of edges inside clusters plus the number of # edges between clusters). This formulation is motivated from a document clustering problem in which one has a pairwise similarity function # learned from past data, and the goal is to partition the current set of documents in a way that correlates with # as much as possible; it can also be viewed as a kind of "agnostic learning" problem. An interesting
Practical privacy: the sulq framework
 In PODS ’05: Proceedings of the twentyfourth ACM SIGMODSIGACTSIGART symposium on Principles of database systems
, 2005
"... We consider a statistical database in which a trusted administrator introduces noise to the query responses with the goal of maintaining privacy of individual database entries. In such a database, a query consists of a pair (S, f) where S is a set of rows in the database and f is a function mapping ..."
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Cited by 223 (35 self)
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We consider a statistical database in which a trusted administrator introduces noise to the query responses with the goal of maintaining privacy of individual database entries. In such a database, a query consists of a pair (S, f) where S is a set of rows in the database and f is a function mapping database rows to {0, 1}. The true answer is P i∈S f(di), and a noisy version is released as the response to the query. Results of Dinur, Dwork, and Nissim show that a strong form of privacy can be maintained using a surprisingly small amount of noise – much less than the sampling error – provided the total number of queries is sublinear in the number of database rows. We call this query and (slightly) noisy reply the SuLQ (SubLinear Queries) primitive. The assumption of sublinearity becomes reasonable as databases grow increasingly large. We extend this work in two ways. First, we modify the privacy analysis to realvalued functions f and arbitrary row types, as a consequence greatly improving the bounds on noise required for privacy. Second, we examine the computational power of the SuLQ primitive. We show that it is very powerful indeed, in that slightly noisy versions of the following computations can be carried out with very few invocations of the primitive: principal component analysis, k means clustering, the Perceptron Algorithm, the ID3 algorithm, and (apparently!) all algorithms that operate in the in the statistical query learning model [11].
Authenticating Pervasive Devices with Human Protocols
, 2005
"... Abstract. Forgery and counterfeiting are emerging as serious security risks in lowcost pervasive computing devices. These devices lack the computational, storage, power, and communication resources necessary for most cryptographic authentication schemes. Surprisingly, lowcost pervasive devices lik ..."
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Cited by 167 (5 self)
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Abstract. Forgery and counterfeiting are emerging as serious security risks in lowcost pervasive computing devices. These devices lack the computational, storage, power, and communication resources necessary for most cryptographic authentication schemes. Surprisingly, lowcost pervasive devices like Radio Frequency Identification (RFID) tags share similar capabilities with another weak computing device: people. These similarities motivate the adoption of techniques from humancomputer security to the pervasive computing setting. This paper analyzes a particular humantocomputer authentication protocol designed by Hopper and Blum (HB), and shows it to be practical for lowcost pervasive devices. We offer an improved, concrete proof of security for the HB protocol against passive adversaries. This paper also offers a new, augmented version of the HB protocol, named HB +, that is secure against active adversaries. The HB + protocol is a novel, symmetric authentication protocol with a simple, lowcost implementation. We prove the security of the HB + protocol against active adversaries based on the hardness of the Learning Parity with Noise (LPN) problem.
Noisetolerant learning, the parity problem, and the statistical query model
 J. ACM
"... We describe a slightly subexponential time algorithm for learning parity functions in the presence of random classification noise. This results in a polynomialtime algorithm for the case of parity functions that depend on only the first O(log n log log n) bits of input. This is the first known ins ..."
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Cited by 165 (2 self)
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We describe a slightly subexponential time algorithm for learning parity functions in the presence of random classification noise. This results in a polynomialtime algorithm for the case of parity functions that depend on only the first O(log n log log n) bits of input. This is the first known instance of an efficient noisetolerant algorithm for a concept class that is provably not learnable in the Statistical Query model of Kearns [7]. Thus, we demonstrate that the set of problems learnable in the statistical query model is a strict subset of those problems learnable in the presence of noise in the PAC model. In codingtheory terms, what we give is a poly(n)time algorithm for decoding linear k × n codes in the presence of random noise for the case of k = clog n log log n for some c> 0. (The case of k O(log n) is trivial since one can just individually check each of the 2 k possible messages and choose the one that yields the closest codeword.) A natural extension of the statistical query model is to allow queries about statistical properties that involve ttuples of examples (as opposed to single examples). The second result of this paper is to show that any class of functions learnable (strongly or weakly) with twise queries for t = O(log n) is also weakly learnable with standard unary queries. Hence this natural extension to the statistical query model does not increase the set of weakly learnable functions. 1.
CostSensitive Learning by CostProportionate Example Weighting
, 2003
"... We propose and evaluate a family of methods for converting classifier learning algorithms and classification theory into costsensitive algorithms and theory. The proposed conversion is based on costproportionate weighting of the training examples, which can be realized either by feeding the weight ..."
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Cited by 161 (15 self)
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We propose and evaluate a family of methods for converting classifier learning algorithms and classification theory into costsensitive algorithms and theory. The proposed conversion is based on costproportionate weighting of the training examples, which can be realized either by feeding the weights to the classification algorithm (as often done in boosting), or by careful subsampling. We give some theoretical performance guarantees on the proposed methods, as well as empirical evidence that they are practical alternatives to existing approaches. In particular, we propose costing, a method based on costproportionate rejection sampling and ensemble aggregation, which achieves excellent predictive performance on two publicly available datasets, while drastically reducing the computation required by other methods.
Enhancing Supervised Learning with Unlabeled Data
, 2000
"... In many practical learning scenarios, there is a small amount of labeled data along with a large pool of unlabeled data. Many supervised learning algorithms have been developed and extensively studied. We present a new "cotraining" strategy for using unlabeled data to improve the pe ..."
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Cited by 156 (1 self)
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In many practical learning scenarios, there is a small amount of labeled data along with a large pool of unlabeled data. Many supervised learning algorithms have been developed and extensively studied. We present a new "cotraining" strategy for using unlabeled data to improve the performance of standard supervised learning algorithms. Unlike much of the prior work, such as the cotraining procedure of Blum and Mitchell (1998), we do not assume there are two redundant views both of which are sufficient for perfect classification. The only requirement our cotraining strategy places on each supervised learning algorithm is that its hypothesis partitions the example space into a set of equivalence classes (e.g. for a decision tree each leaf defines an equivalence class). We evaluate our cotraining strategy via experiments using data from the UCI repository. 1. Introduction In many practical learning scenarios, there is a small amount of labeled data along with a lar...
Partially Supervised Classification of Text Documents
, 2002
"... We investigate the following problem: Given a set of documents of a particular topic or class # , and a large set # of mixed documents that contains documents from class # and other types of documents, identify the documents from class # in # . The key feature of this problem is that there is n ..."
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Cited by 141 (23 self)
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We investigate the following problem: Given a set of documents of a particular topic or class # , and a large set # of mixed documents that contains documents from class # and other types of documents, identify the documents from class # in # . The key feature of this problem is that there is no labeled non # document, which makes traditional machine learning techniques inapplicable, as they all need labeled documents of both classes. We call this problem partially supervised classification. In this paper, we show that this problem can be posed as a constrained optimization problem and that under appropriate conditions, solutions to the constrained optimization problem will give good solutions to the partially supervised classification problem. We present a novel technique to solve the problem and demonstrate the effectiveness of the technique through extensive experimentation.