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Bimodal Loglinear Regression for Fusion of Audio and Visual Features
"... One of the most commonly used audiovisual fusion approaches is featurelevel fusion where the audio and visual features are concatenated. Although this approach has been successfully used in several applications, it does not take into account interactions between the features, which can be a problem ..."
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visual features. To this end, we propose a loglinear model, named Bimodal Loglinear regression, which accounts for interactions between the features of the two modalities. The performance of the target classifiers is measured in the task of laughtervsspeech discrimination, since both laughter and speech
Linear Model as a Threshold Indicator LogLinear Regression Results
"... Conclusion 4 The US EPA has developed an estimate of the human cancer risk from dioxin, using the standard lowdose linear extrapolation approach. This estimate has been controversial, because of concern that it may overestimate the cancer risk. An alternative approach has been published, and was pr ..."
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Conclusion 4 The US EPA has developed an estimate of the human cancer risk from dioxin, using the standard lowdose linear extrapolation approach. This estimate has been controversial, because of concern that it may overestimate the cancer risk. An alternative approach has been published
Towards an early software estimation using loglinear regression and a multilayer perceptron model
 Journal of Systems and Software
"... Citation of this paper: ..."
COVAR: Computer Program for Multifactor Relative Risks and Tests of Hypotheses Using a VarianceCovariance Matrix from Linear and LogLinear Regression
, 1997
"... A computer program for multifactor relative risks, condence limits, and tests of hypotheses using regression coecients and a variancecovariance matrix obtained from a previous additive or multiplicative regression analysis is described in detail. Data used by the program can be stored and input fro ..."
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A computer program for multifactor relative risks, condence limits, and tests of hypotheses using regression coecients and a variancecovariance matrix obtained from a previous additive or multiplicative regression analysis is described in detail. Data used by the program can be stored and input
Predicting the Semantic Orientation of Adjectives
, 1997
"... We identify and validate from a large corpus constraints from conjunctions on the positive or negative semantic orientation of the conjoined adjectives. A loglinear regression model uses these constraints to predict whether conjoined adjectives are of same or different orientations, achiev ..."
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Cited by 460 (5 self)
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We identify and validate from a large corpus constraints from conjunctions on the positive or negative semantic orientation of the conjoined adjectives. A loglinear regression model uses these constraints to predict whether conjoined adjectives are of same or different orientations, achiev
Structured Penalties for Loglinear Language Models
"... Language models can be formalized as loglinear regression models where the input features represent previously observed contexts up to a certain length m. The complexity of existing algorithms to learn the parameters by maximum likelihood scale linearly in nd, where n is the length of the training ..."
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Language models can be formalized as loglinear regression models where the input features represent previously observed contexts up to a certain length m. The complexity of existing algorithms to learn the parameters by maximum likelihood scale linearly in nd, where n is the length
Structured Penalties for Loglinear Language Models
"... Language models can be formalized as loglinear regression models where the input features represent previously observed contexts up to a certain length m. The complexity of existing algorithms to learn the parameters by maximum likelihood scale linearly in nd, where n is the length of the training ..."
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Language models can be formalized as loglinear regression models where the input features represent previously observed contexts up to a certain length m. The complexity of existing algorithms to learn the parameters by maximum likelihood scale linearly in nd, where n is the length
Quantile Regression
 JOURNAL OF ECONOMIC PERSPECTIVES—VOLUME 15, NUMBER 4—FALL 2001—PAGES 143–156
, 2001
"... We say that a student scores at the fifth quantile of a standardized exam if he performs better than the proportion � of the reference group of students and worse than the proportion (1–�). Thus, half of students perform better than the median student and half perform worse. Similarly, the quartiles ..."
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Cited by 937 (10 self)
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, the quartiles divide the population into four segments with equal proportions of the reference population in each segment. The quintiles divide the population into five parts; the deciles into ten parts. The quantiles, or percentiles, or occasionally fractiles, refer to the general case. Quantile regression
Least Median of Squares Regression
 JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
, 1984
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