The Experts below are selected from a list of 587442 Experts worldwide ranked by ideXlab platform
Bradley H Shaffer - One of the best experts on this subject based on the ideXlab platform.
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incorporating Model Complexity and spatial sampling bias into ecological niche Models of climate change risks faced by 90 california vertebrate species of concern
Diversity and Distributions, 2014Co-Authors: Dan L Warren, Amber N Wright, Stephanie N Seifert, Bradley H ShafferAbstract:Aim Ecological niche Models are increasingly being used to aid in predicting the effects of future climate change on species distributions. Complex Models that show high predictive performance on current distribution data may do a poor job of predicting new data due to overfitting. In addition, Model performance is often evaluated using techniques that are sensitive to spatial sampling bias. Here, we explore the effects of Model Complexity and spatial sampling bias on niche Models for 90 vertebrate taxa of conservation concern.
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incorporating Model Complexity and spatial sampling bias into ecological niche Models of climate change risks faced by 90 california vertebrate species of concern
Diversity and Distributions, 2014Co-Authors: Dan L Warren, Amber N Wright, Stephanie N Seifert, Bradley H ShafferAbstract:Aim Ecological niche Models are increasingly being used to aid in predicting the effects of future climate change on species distributions. Complex Models that show high predictive performance on current distribution data may do a poor job of predicting new data due to overfitting. In addition, Model performance is often evaluated using techniques that are sensitive to spatial sampling bias. Here, we explore the effects of Model Complexity and spatial sampling bias on niche Models for 90 vertebrate taxa of conservation concern.
Dan L Warren - One of the best experts on this subject based on the ideXlab platform.
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incorporating Model Complexity and spatial sampling bias into ecological niche Models of climate change risks faced by 90 california vertebrate species of concern
Diversity and Distributions, 2014Co-Authors: Dan L Warren, Amber N Wright, Stephanie N Seifert, Bradley H ShafferAbstract:Aim Ecological niche Models are increasingly being used to aid in predicting the effects of future climate change on species distributions. Complex Models that show high predictive performance on current distribution data may do a poor job of predicting new data due to overfitting. In addition, Model performance is often evaluated using techniques that are sensitive to spatial sampling bias. Here, we explore the effects of Model Complexity and spatial sampling bias on niche Models for 90 vertebrate taxa of conservation concern.
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incorporating Model Complexity and spatial sampling bias into ecological niche Models of climate change risks faced by 90 california vertebrate species of concern
Diversity and Distributions, 2014Co-Authors: Dan L Warren, Amber N Wright, Stephanie N Seifert, Bradley H ShafferAbstract:Aim Ecological niche Models are increasingly being used to aid in predicting the effects of future climate change on species distributions. Complex Models that show high predictive performance on current distribution data may do a poor job of predicting new data due to overfitting. In addition, Model performance is often evaluated using techniques that are sensitive to spatial sampling bias. Here, we explore the effects of Model Complexity and spatial sampling bias on niche Models for 90 vertebrate taxa of conservation concern.
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ecological niche Modeling in maxent the importance of Model Complexity and the performance of Model selection criteria
Ecological Applications, 2011Co-Authors: Dan L Warren, Stephanie N SeifertAbstract:Maxent, one of the most commonly used methods for inferring species distributions and environmental tolerances from occurrence data, allows users to fit Models of arbitrary Complexity. Model Complexity is typically constrained via a process known as L1 regularization, but at present little guidance is available for setting the appropriate level of regularization, and the effects of inappropriately complex or simple Models are largely unknown. In this study, we demonstrate the use of information criterion approaches to setting regularization in Maxent, and we compare Models selected using information criteria to Models selected using other criteria that are common in the literature. We evaluate Model performance using occurrence data generated from a known "true" initial Maxent Model, using several different metrics for Model quality and transferability. We demonstrate that Models that are inappropriately complex or inappropriately simple show reduced ability to infer habitat quality, reduced ability to infer the relative importance of variables in constraining species' distributions, and reduced transferability to other time periods. We also demonstrate that information criteria may offer significant advantages over the methods commonly used in the literature.
Stephanie N Seifert - One of the best experts on this subject based on the ideXlab platform.
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incorporating Model Complexity and spatial sampling bias into ecological niche Models of climate change risks faced by 90 california vertebrate species of concern
Diversity and Distributions, 2014Co-Authors: Dan L Warren, Amber N Wright, Stephanie N Seifert, Bradley H ShafferAbstract:Aim Ecological niche Models are increasingly being used to aid in predicting the effects of future climate change on species distributions. Complex Models that show high predictive performance on current distribution data may do a poor job of predicting new data due to overfitting. In addition, Model performance is often evaluated using techniques that are sensitive to spatial sampling bias. Here, we explore the effects of Model Complexity and spatial sampling bias on niche Models for 90 vertebrate taxa of conservation concern.
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incorporating Model Complexity and spatial sampling bias into ecological niche Models of climate change risks faced by 90 california vertebrate species of concern
Diversity and Distributions, 2014Co-Authors: Dan L Warren, Amber N Wright, Stephanie N Seifert, Bradley H ShafferAbstract:Aim Ecological niche Models are increasingly being used to aid in predicting the effects of future climate change on species distributions. Complex Models that show high predictive performance on current distribution data may do a poor job of predicting new data due to overfitting. In addition, Model performance is often evaluated using techniques that are sensitive to spatial sampling bias. Here, we explore the effects of Model Complexity and spatial sampling bias on niche Models for 90 vertebrate taxa of conservation concern.
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ecological niche Modeling in maxent the importance of Model Complexity and the performance of Model selection criteria
Ecological Applications, 2011Co-Authors: Dan L Warren, Stephanie N SeifertAbstract:Maxent, one of the most commonly used methods for inferring species distributions and environmental tolerances from occurrence data, allows users to fit Models of arbitrary Complexity. Model Complexity is typically constrained via a process known as L1 regularization, but at present little guidance is available for setting the appropriate level of regularization, and the effects of inappropriately complex or simple Models are largely unknown. In this study, we demonstrate the use of information criterion approaches to setting regularization in Maxent, and we compare Models selected using information criteria to Models selected using other criteria that are common in the literature. We evaluate Model performance using occurrence data generated from a known "true" initial Maxent Model, using several different metrics for Model quality and transferability. We demonstrate that Models that are inappropriately complex or inappropriately simple show reduced ability to infer habitat quality, reduced ability to infer the relative importance of variables in constraining species' distributions, and reduced transferability to other time periods. We also demonstrate that information criteria may offer significant advantages over the methods commonly used in the literature.
Mukund Padmanabhan - One of the best experts on this subject based on the ideXlab platform.
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Model Complexity adaptation using a discriminant measure
IEEE Transactions on Speech and Audio Processing, 2000Co-Authors: Mukund Padmanabhan, L R BanAbstract:We present a discriminant measure that can be used to determine the Model Complexity in a speech recognition system. In the speech recognition process, sub-phonetic classes are Modelled as mixtures of Gaussians, and we present a new discriminant measure that uses the classification accuracy to determine in an objective fashion, the number of Gaussians required to best Model the PDF of an allophone class. We compare the performance of this criterion with other criteria such as the Bayesian information criterion (BIC), and show that the BIC and the discriminative criterion lead to parsimonious Models that provide the same word error rate performance as much larger baseline systems. However, this performance improvement depends on the size of the system, and there appears to be a crossover point beyond which both the BIC and the discriminative criterion are worse than a much simpler criterion. The discriminative criterion also enables this crossover point to be controlled by means of a threshold that is used in the criterion, and can lead to a better tradeoff of Complexity versus word error rate.
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a discriminant measure for Model Complexity adaptation
International Conference on Acoustics Speech and Signal Processing, 1998Co-Authors: Lalit R Bahl, Mukund PadmanabhanAbstract:We present a discriminant measure that can be used to determine the Model Complexity in a speech recognition system. In the speech recognition process, given a test feature vector the conditional probability of the feature vector has to be obtained for several allophone (sub-phonetic units) classes using a Gaussian-mixture density Model for each class. The Gaussian-mixture Models are constructed from the training data belonging to the allophone classes, and the number of mixture components that are required to adequately Model the PDF of each class is determined by using some simple rule of thumb-for instance the number of components has to be sufficient to Model the data reasonably well but not so many as to overModel the data. A typical example of the choice of the number is to make it proportional to the number of data samples. However, such methods may result in Models that are sub-optimal as far as classification accuracy is concerned. We present a new discriminant measure that can be used to determine in an objective fashion, the number of Gaussians required to best Model the PDF of an allophone class. We also present the results of experiments showing the improvement in recognition performance when the number of mixture components is chosen based on the discriminant measure as opposed to the rule of thumb. These results are presented both for the speaker-independent and speaker-adapted case.
Amber N Wright - One of the best experts on this subject based on the ideXlab platform.
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incorporating Model Complexity and spatial sampling bias into ecological niche Models of climate change risks faced by 90 california vertebrate species of concern
Diversity and Distributions, 2014Co-Authors: Dan L Warren, Amber N Wright, Stephanie N Seifert, Bradley H ShafferAbstract:Aim Ecological niche Models are increasingly being used to aid in predicting the effects of future climate change on species distributions. Complex Models that show high predictive performance on current distribution data may do a poor job of predicting new data due to overfitting. In addition, Model performance is often evaluated using techniques that are sensitive to spatial sampling bias. Here, we explore the effects of Model Complexity and spatial sampling bias on niche Models for 90 vertebrate taxa of conservation concern.
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incorporating Model Complexity and spatial sampling bias into ecological niche Models of climate change risks faced by 90 california vertebrate species of concern
Diversity and Distributions, 2014Co-Authors: Dan L Warren, Amber N Wright, Stephanie N Seifert, Bradley H ShafferAbstract:Aim Ecological niche Models are increasingly being used to aid in predicting the effects of future climate change on species distributions. Complex Models that show high predictive performance on current distribution data may do a poor job of predicting new data due to overfitting. In addition, Model performance is often evaluated using techniques that are sensitive to spatial sampling bias. Here, we explore the effects of Model Complexity and spatial sampling bias on niche Models for 90 vertebrate taxa of conservation concern.
