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2,560
Training Linear SVMs in Linear Time
, 2006
"... Linear Support Vector Machines (SVMs) have become one of the most prominent machine learning techniques for highdimensional sparse data commonly encountered in applications like text classification, wordsense disambiguation, and drug design. These applications involve a large number of examples n ..."
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Cited by 549 (6 self)
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as well as a large number of features N, while each example has only s << N nonzero features. This paper presents a CuttingPlane Algorithm for training linear SVMs that provably has training time O(sn) for classification problems and O(sn log(n)) for ordinal regression problems. The algorithm
Mean shift: A robust approach toward feature space analysis
 In PAMI
, 2002
"... A general nonparametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure, the mean shift. We prove for discrete data the convergence ..."
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Cited by 2395 (37 self)
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A general nonparametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure, the mean shift. We prove for discrete data
Apoptosis: a Basic Biological Phenomenon with Wideranging Implications in Tissue Kinetics
 Br. J. Cancer
, 1972
"... Summary.The term apoptosis is proposed for a hitherto little recognized mechanism of controlled cell deletion, which appears to play a complementary but opposite role to mitosis in the regulation of animal cell populations. Its morphological features suggest that it is an active, inherently program ..."
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Cited by 641 (6 self)
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programmed phenomenon, and it has been shown that it can be initiated or inhibited by a variety of environmental stimuli, both physiological and pathological. The structural changes take place in two discrete stages. The first comprises nuclear and cytoplasmic condensation and breaking up of the cell into a
The control of the false discovery rate in multiple testing under dependency
 Annals of Statistics
, 2001
"... Benjamini and Hochberg suggest that the false discovery rate may be the appropriate error rate to control in many applied multiple testing problems. A simple procedure was given there as an FDR controlling procedure for independent test statistics and was shown to be much more powerful than comparab ..."
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Cited by 1093 (16 self)
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comparable procedures which control the traditional familywise error rate. We prove that this same procedure also controls the false discovery rate when the test statistics have positive regression dependency on each of the test statistics corresponding to the true null hypotheses. This condition
How important is methodology for the estimates of the determinants of happiness
 Economic Journal
, 2004
"... Psychologists and sociologists usually interpret happiness scores as cardinal and comparable across respondents, and thus run OLS regressions on happiness and changes in happiness. Economists usually assume only ordinality and have mainly used ordered latent response models, thereby not taking satis ..."
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Cited by 406 (14 self)
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Psychologists and sociologists usually interpret happiness scores as cardinal and comparable across respondents, and thus run OLS regressions on happiness and changes in happiness. Economists usually assume only ordinality and have mainly used ordered latent response models, thereby not taking
Flexible smoothing with Bsplines and penalties
 STATISTICAL SCIENCE
, 1996
"... Bsplines are attractive for nonparametric modelling, but choosing the optimal number and positions of knots is a complex task. Equidistant knots can be used, but their small and discrete number allows only limited control over smoothness and fit. We propose to use a relatively large number of knots ..."
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Cited by 405 (7 self)
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Bsplines are attractive for nonparametric modelling, but choosing the optimal number and positions of knots is a complex task. Equidistant knots can be used, but their small and discrete number allows only limited control over smoothness and fit. We propose to use a relatively large number
Correlationbased feature selection for discrete and numeric class machine learning
, 2000
"... Algorithms for feature selection fall into two broad categories: wrappers use the learning algorithm itself to evaluate the usefulness of features, while lters evaluate features according to heuristics based on general characteristics of the data. For application to large databases, lters have prove ..."
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Cited by 267 (2 self)
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proven to be more practical than wrappers because they are much faster. However, most existing lter algorithms only work with discrete classi cation problems. This paper describes a fast, correlationbased lter algorithm that can be applied to continuous and discrete problems. Experiments using the new
Feature selection for ordinal regression
 In Proceedings of the 25th ACM Symposium on Applied Computing (SAC’10
, 2010
"... Ordinal regression (also known as ordinal classification) is a supervised learning task that consists of automatically determining the implied rating of a data item on a fixed, discrete rating scale. This problem is receiving increasing attention from the sentiment analysis and opinion mining commun ..."
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Cited by 8 (6 self)
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Ordinal regression (also known as ordinal classification) is a supervised learning task that consists of automatically determining the implied rating of a data item on a fixed, discrete rating scale. This problem is receiving increasing attention from the sentiment analysis and opinion mining
Gaussian processes for ordinal regression
 Journal of Machine Learning Research
, 2004
"... We present a probabilistic kernel approach to ordinal regression based on Gaussian processes. A threshold model that generalizes the probit function is used as the likelihood function for ordinal variables. Two inference techniques, based on the Laplace approximation and the expectation propagation ..."
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Cited by 116 (4 self)
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We present a probabilistic kernel approach to ordinal regression based on Gaussian processes. A threshold model that generalizes the probit function is used as the likelihood function for ordinal variables. Two inference techniques, based on the Laplace approximation and the expectation propagation
Loss functions for preference levels: Regression with discrete ordered labels
 Proceedings of the IJCAI Multidisciplinary Workshop on Advances in Preference Handling
, 2005
"... We consider different types of loss functions for discrete ordinal regression, i.e. fitting labels that may take one of several discrete, but ordered, values. These types of labels arise when preferences are specified by selecting, for each item, one of several rating “levels”, e.g. one through five ..."
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Cited by 16 (3 self)
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We consider different types of loss functions for discrete ordinal regression, i.e. fitting labels that may take one of several discrete, but ordered, values. These types of labels arise when preferences are specified by selecting, for each item, one of several rating “levels”, e.g. one through
Results 1  10
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