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"... BiasVariance tradeoff in Hybrid GenerativeDiscriminative models Given any generative classifier based on an inexact density model, we can define a discriminative counterpart that reduces its asymptotic error rate, while increasing the estimation variance. An optimal biasvariance balance might be ..."
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BiasVariance tradeoff in Hybrid GenerativeDiscriminative models Given any generative classifier based on an inexact density model, we can define a discriminative counterpart that reduces its asymptotic error rate, while increasing the estimation variance. An optimal biasvariance balance might
Exploiting Generative Models in Discriminative Classifiers
 In Advances in Neural Information Processing Systems 11
, 1998
"... Generative probability models such as hidden Markov models provide a principled way of treating missing information and dealing with variable length sequences. On the other hand, discriminative methods such as support vector machines enable us to construct flexible decision boundaries and often resu ..."
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Cited by 538 (11 self)
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Generative probability models such as hidden Markov models provide a principled way of treating missing information and dealing with variable length sequences. On the other hand, discriminative methods such as support vector machines enable us to construct flexible decision boundaries and often
Conceptual complexity and the biasvariance tradeoff
 In Proceedings of the 28th Annual Conference of the Cognitive Science Society. Mahwah, NJ: Erlbaum
, 2006
"... In this paper we propose that the dichotomy between exemplarbased and prototypebased models of concept learning can be regarded as an instance of the tradeoff between complexity and datafit, often referred to in the statistical learning literature as the biasvariance tradeoff. This continuum ref ..."
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Cited by 8 (3 self)
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In this paper we propose that the dichotomy between exemplarbased and prototypebased models of concept learning can be regarded as an instance of the tradeoff between complexity and datafit, often referred to in the statistical learning literature as the biasvariance tradeoff. This continuum
On Discriminative vs. Generative classifiers: A comparison of logistic regression and naive Bayes
, 2001
"... We compare discriminative and generative learning as typified by logistic regression and naive Bayes. We show, contrary to a widely held belief that discriminative classifiers are almost always to be preferred, that there can often be two distinct regimes of performance as the training set size is i ..."
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Cited by 513 (8 self)
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We compare discriminative and generative learning as typified by logistic regression and naive Bayes. We show, contrary to a widely held belief that discriminative classifiers are almost always to be preferred, that there can often be two distinct regimes of performance as the training set size
Hybrid GenerativeDiscriminative Visual Categorization.
"... Learning models for detecting and classifying object categories is a challenging problem in machine vision. While discriminative approaches to learning and classification have, in principle, superior performance, generative approaches provide many useful features, one of which is the ability to natu ..."
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Cited by 5 (1 self)
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to naturally establish explicit correspondence between model components and scene features – this, in turn, allows for the handling of missing data and unsupervised learning in clutter. We explore a hybrid generative/discriminative approach, using ‘Fisher Kernels’ [12], which retains most of the desirable
Hierarchical Models of Object Recognition in Cortex
, 1999
"... The classical model of visual processing in cortex is a hierarchy of increasingly sophisticated representations, extending in a natural way the model of simple to complex cells of Hubel and Wiesel. Somewhat surprisingly, little quantitative modeling has been done in the last 15 years to explore th ..."
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Cited by 817 (84 self)
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The classical model of visual processing in cortex is a hierarchy of increasingly sophisticated representations, extending in a natural way the model of simple to complex cells of Hubel and Wiesel. Somewhat surprisingly, little quantitative modeling has been done in the last 15 years to explore
Interpretation of Hybrid Generative/Discriminative Algorithms
"... In discriminant analysis, probabilistic generative and discriminative approaches represent two paradigms of statistical modelling and learning. In order to exploit the best of both worlds, hybrid modelling and learning techniques have attracted much research interest recently, one example being the ..."
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In discriminant analysis, probabilistic generative and discriminative approaches represent two paradigms of statistical modelling and learning. In order to exploit the best of both worlds, hybrid modelling and learning techniques have attracted much research interest recently, one example being
ModelBased Clustering, Discriminant Analysis, and Density Estimation
 JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
, 2000
"... Cluster analysis is the automated search for groups of related observations in a data set. Most clustering done in practice is based largely on heuristic but intuitively reasonable procedures and most clustering methods available in commercial software are also of this type. However, there is little ..."
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Cited by 557 (28 self)
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for modelbased clustering that provides a principled statistical approach to these issues. We also show that this can be useful for other problems in multivariate analysis, such as discriminant analysis and multivariate density estimation. We give examples from medical diagnosis, mineeld detection, cluster
Toward a model of text comprehension and production
 Psychological Review
, 1978
"... The semantic structure of texts can be described both at the local microlevel and at a more global macrolevel. A model for text comprehension based on this notion accounts for the formation of a coherent semantic text base in terms of a cyclical process constrained by limitations of working memory. ..."
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Cited by 540 (12 self)
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are predictable only when the control schema can be made explicit. On the production side, the model is concerned with the generation of recall and summarization protocols. This process is partly reproductive and partly constructive, involving the inverse operation of the macrooperators. The model is applied
Maximum Likelihood Linear Transformations for HMMBased Speech Recognition
 Computer Speech and Language
, 1998
"... This paper examines the application of linear transformations for speaker and environmental adaptation in an HMMbased speech recognition system. In particular, transformations that are trained in a maximum likelihood sense on adaptation data are investigated. Other than in the form of a simple bias ..."
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Cited by 538 (65 self)
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bias, strict linear featurespace transformations are inappropriate in this case. Hence, only modelbased linear transforms are considered. The paper compares the two possible forms of modelbased transforms: (i) unconstrained, where any combination of mean and variance transform may be used, and (ii
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