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91
Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2006
"... We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semisupervised framework that incorporates labeled and unlabeled data in a generalpurpose learner. Some transductive graph learning al ..."
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Cited by 343 (13 self)
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We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semisupervised framework that incorporates labeled and unlabeled data in a generalpurpose learner. Some transductive graph learning algorithms and standard methods including Support Vector Machines and Regularized Least Squares can be obtained as special cases. We utilize properties of Reproducing Kernel Hilbert spaces to prove new Representer theorems that provide theoretical basis for the algorithms. As a result (in contrast to purely graphbased approaches) we obtain a natural outofsample extension to novel examples and so are able to handle both transductive and truly semisupervised settings. We present experimental evidence suggesting that our semisupervised algorithms are able to use unlabeled data effectively. Finally we have a brief discussion of unsupervised and fully supervised learning within our general framework.
In defense of onevsall classification
 Journal of Machine Learning Research
, 2004
"... Editor: John ShaweTaylor We consider the problem of multiclass classification. Our main thesis is that a simple “onevsall ” scheme is as accurate as any other approach, assuming that the underlying binary classifiers are welltuned regularized classifiers such as support vector machines. This the ..."
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Cited by 210 (0 self)
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Editor: John ShaweTaylor We consider the problem of multiclass classification. Our main thesis is that a simple “onevsall ” scheme is as accurate as any other approach, assuming that the underlying binary classifiers are welltuned regularized classifiers such as support vector machines. This thesis is interesting in that it disagrees with a large body of recent published work on multiclass classification. We support our position by means of a critical review of the existing literature, a substantial collection of carefully controlled experimental work, and theoretical arguments.
The mathematics of learning: Dealing with data
 Notices of the American Mathematical Society
, 2003
"... Draft for the Notices of the AMS Learning is key to developing systems tailored to a broad range of data analysis and information extraction tasks. We outline the mathematical foundations of learning theory and describe a key algorithm of it. 1 ..."
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Cited by 109 (15 self)
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Draft for the Notices of the AMS Learning is key to developing systems tailored to a broad range of data analysis and information extraction tasks. We outline the mathematical foundations of learning theory and describe a key algorithm of it. 1
Regularized LeastSquares Classification
"... We consider the solution of binary classification problems via Tikhonov regularization in a Reproducing Kernel Hilbert Space using the square loss, and denote the resulting algorithm Regularized LeastSquares Classification (RLSC). We sketch ..."
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Cited by 62 (1 self)
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We consider the solution of binary classification problems via Tikhonov regularization in a Reproducing Kernel Hilbert Space using the square loss, and denote the resulting algorithm Regularized LeastSquares Classification (RLSC). We sketch
Finding latent code errors via machine learning over program executions
 In ICSE
, 2004
"... This paper proposes a technique for identifying program properties that indicate errors. The technique generates machine learning models of program properties known to result from errors, and applies these models to program properties of userwritten code to classify and rank properties that may lea ..."
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Cited by 58 (6 self)
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This paper proposes a technique for identifying program properties that indicate errors. The technique generates machine learning models of program properties known to result from errors, and applies these models to program properties of userwritten code to classify and rank properties that may lead the user to errors. Given a set of properties produced by the program analysis, the technique selects a subset of properties that are most likely to reveal an error. An implementation, the Fault Invariant Classifier, demonstrates the efficacy of the technique. The implementation uses dynamic invariant detection to generate program properties. It uses support vector machine and decision tree learning tools to classify those properties. In our experimental evaluation, the technique increases the relevance (the concentration of faultrevealing properties) by a factor of 50 on average for the C programs, and 4.8 for the Java programs. Preliminary experience suggests that most of the faultrevealing properties do lead a programmer to an error. 1
Efficient Kernel Machines Using the Improved Fast Gauss Transform
 Advances in Neural Information Processing Systems 17
, 2004
"... The computation required for kernel machines with N training samples is O(N ). Such computational complexity is significant even for moderate size problems and is prohibitive for large datasets. We present an approximation technique based on the improved fast Gauss transform to reduce the com ..."
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Cited by 42 (6 self)
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The computation required for kernel machines with N training samples is O(N ). Such computational complexity is significant even for moderate size problems and is prohibitive for large datasets. We present an approximation technique based on the improved fast Gauss transform to reduce the computation to O(N). We also give an error bound for the approximation, and provide experimental results on the UCI datasets.
Graph Kernels and Gaussian Processes for Relational Reinforcement Learning
 Machine Learning
, 2003
"... Relational reinforcement learning is a Qlearning technique for relational stateaction spaces. It aims to enable agents to learn how to act in an environment that has no natural representation as a tuple of constants. In this case, the learning algorithm used to approximate the mapping between stat ..."
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Cited by 41 (9 self)
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Relational reinforcement learning is a Qlearning technique for relational stateaction spaces. It aims to enable agents to learn how to act in an environment that has no natural representation as a tuple of constants. In this case, the learning algorithm used to approximate the mapping between stateaction pairs and their so called Q(uality)value has to be not only very reliable, but it also has to be able to handle the relational representation of stateaction pairs. In this paper we investigate...
Documentword coregularization for semisupervised sentiment analysis
 IN ICDM. VIKAS SINDHWANI, JIANYING HU, AND ALEXANDRA MOJSILOVIC
, 2008
"... The goal of sentiment prediction is to automatically identify whether a given piece of text expresses positive or negative opinion towards a topic of interest. One can pose sentiment prediction as a standard text categorization problem. However, gathering labeled data turns out to be a bottleneck in ..."
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Cited by 30 (10 self)
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The goal of sentiment prediction is to automatically identify whether a given piece of text expresses positive or negative opinion towards a topic of interest. One can pose sentiment prediction as a standard text categorization problem. However, gathering labeled data turns out to be a bottleneck in the process of building high quality text classifiers. Fortunately, background knowledge is often available in the form of prior information about the sentiment polarity of words in a lexicon. Moreover, in many applications abundant unlabeled data is also available. In this paper, we propose a novel semisupervised sentiment prediction algorithm that utilizes lexical prior knowledge in conjunction with unlabeled examples. Our method is based on joint sentiment analysis of documents and words based on a bipartite graph representation of the data. We present an empirical study on a diverse collection of sentiment prediction problems which confirms that our semisupervised lexical models significantly outperform purely supervised and competing semisupervised techniques.
Musical QuerybyDescription as a Multiclass Learning Problem
 In Proc. IEEE Multimedia Signal Processing Conference (MMSP
, 2002
"... We present the querybydescription (QBD) component of "Kandem," a timeaware music retrieval system. The QBD system we describe learns a relation between descriptive text concerning a musical artist and their actual acoustic output, making such queries as "Play me something loud with ..."
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Cited by 27 (1 self)
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We present the querybydescription (QBD) component of "Kandem," a timeaware music retrieval system. The QBD system we describe learns a relation between descriptive text concerning a musical artist and their actual acoustic output, making such queries as "Play me something loud with an electronic beat" possible by merely analyzing the audio content of a database. We show a novel machine learning technique based on Regularized LeastSquares Classification (RLSC) that can quickly and efficiently learn the nonlinear relation between descriptive language and audio features by treating the problem as a large number of possible output classes linked to the same set of input features. We show how the RLSC training can easily eliminate irrelevant labels. I.
Similaritybased Classification: Concepts and Algorithms
, 2008
"... This report reviews and extends the field of similaritybased classification, presenting new analyses, algorithms, data sets, and the most comprehensive set of experimental results to date. Specifically, the generalizability of using similarities as features is analyzed, design goals and methods for ..."
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Cited by 26 (2 self)
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This report reviews and extends the field of similaritybased classification, presenting new analyses, algorithms, data sets, and the most comprehensive set of experimental results to date. Specifically, the generalizability of using similarities as features is analyzed, design goals and methods for weighting nearestneighbors for similaritybased learning are proposed, and different methods for consistently converting similarities into kernels are compared. Experiments on eight real data sets compare eight approaches and their variants to similaritybased learning. 1