Search Results - "Girosi, F."

Incorporating prior information in machine learning by creating virtual examples by Niyogi, P., Girosi, F., Poggio, T.

“…One of the key problems in supervised learning is the insufficient size of the training set. The natural way for an intelligent learner to counter this problem…”
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Networks for approximation and learning by Poggio, T., Girosi, F.

“…The problem of the approximation of nonlinear mapping, (especially continuous mappings) is considered. Regularization theory and a theoretical framework for…”
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Comparing support vector machines with Gaussian kernels to radial basis function classifiers by Scholkopf, B., Kah-Kay Sung, Burges, C.J.C., Girosi, F., Niyogi, P., Poggio, T., Vapnik, V.

“…The support vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural…”
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Regularization Algorithms for Learning that are Equivalent to Multilayer Networks by Poggio, T., Girosi, F.

“…Learning an input-output mapping from a set of examples, of the type that many neural networks have been constructed to perform, can be regarded as…”
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Parallel and deterministic algorithms from MRFs: surface reconstruction by Geiger, D., Girosi, F.

“…Deterministic approximations to Markov random field (MRF) models are derived. One of the models is shown to give in a natural way the graduated nonconvexity…”
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Networks and the best approximation property by GIROSI, F, POGGIO, T

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A computational approach to motion perception by URAS, S, GIROSI, F, VERRI, A, TORRE, V

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Socioeconomic Correlates of Eating Disorder Symptoms in an Australian Population-Based Sample by Mulders-Jones, Brittany, Mitchison, Deborah, Girosi, Federico, Hay, Phillipa

“…Recent research has challenged the stereotype that eating disorders are largely limited to young, White, upper-class females. This study investigated the…”
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Can Electronic Medical Record Systems Transform Health Care? Potential Health Benefits, Savings, And Costs by Hillestad, Richard, Bigelow, James, Bower, Anthony, Girosi, Federico, Meili, Robin, Scoville, Richard, Taylor, Roger

“…To broadly examine the potential health and financial benefits of health information technology (HIT), this paper compares health care with the use of IT in…”
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PP50People with multiple unhealthy lifestyles are less likely to consult primary healthcare. Evidence from 267,153 Australians by Feng, X, Girosi, F, McRae, I

“…BackgroundInterventions for multiple unhealthy lifestyles are being implemented within primary healthcare settings. This strategy assumes that people with…”
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An improved training algorithm for support vector machines by Osuna, E., Freund, R., Girosi, F.

“…We investigate the problem of training a support vector machine (SVM) on a very large database in the case in which the number of support vectors is also very…”
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Sparse correlation kernel reconstruction by Papageorgiou, C., Girosi, F., Poggio, T.

“…This paper presents a new paradigm for signal reconstruction and superresolution, correlation kernel analysis (CKA), that is based on the selection of a sparse…”
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Nonlinear prediction of chaotic time series using support vector machines by Mukherjee, S., Osuna, E., Girosi, F.

“…A novel method for regression has been recently proposed by Vapnik et al. (1995, 1996). The technique, called support vector machine (SVM), is very well…”
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Promoting Health Information Technology: Is There A Case For More-Aggressive Government Action? by Taylor, Roger, Bower, Anthony, Girosi, Federico, Bigelow, James, Fonkych, Kateryna, Hillestad, Richard

“…Health information technology (HIT) could save $81-$162 billion or more annually while greatly reducing morbidity and mortality. However, gaining these…”
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Training support vector machines: an application to face detection by Osuna, E., Freund, R., Girosit, F.

“…We investigate the application of Support Vector Machines (SVMs) in computer vision. SVM is a learning technique developed by V. Vapnik and his team (AT&T Bell…”
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From regularization to radial, tensor and additive splines by Poggio, T., Girosi, F., Jones, M.

“…We had previously shown that regularization principles lead to approximation schemes which are equivalent to networks with one layer of hidden units, called…”
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Incorporating prior information in machine learning by creating virtual examples: Intelligent signal processing by NIYOGI, P, GIROSI, F, POGGIO, T

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Networks for approximation and learning: Neural networks. I, Theory and modeling by POGGIO, T, GIROSI, F

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Comparing support vector machines with Gaussian kernels to radial basis function classifiers: Neural networks by SCHÖLKOPF, B, SUNG, K.-K, BURGES, C. J. C, GIROSI, F, NIYOGI, P, POGGIO, T, VAPNIK, V

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Prior knowledge and the creation of "virtual" examples for RBF networks by Girosi, F., Chan, N.T.

“…Considers the problem of how to incorporate prior knowledge in supervised learning techniques. The authors set the problem in the framework of regularization…”
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