منابع مشابه
Mutual Information and Conditional Mean Prediction Error
Mutual information is fundamentally important for measuring statistical dependence between variables and for quantifying information transfer by signaling and communication mechanisms. It can, however, be challenging to evaluate for physical models of such mechanisms and to estimate reliably from data. Furthermore, its relationship to better known statistical procedures is still poorly understo...
متن کاملA Mutual Information Based Ensemble Method to Estimate Bayes Error
Determining the performance bounds possible with a particular clas-siier or data set is often of great importance in pattern recognition applications. A previously introduced method for Bayes error estimation based on combining multiple classiiers outperforms more traditional estimates of this error in many instances. The accuracy of this estimate, however, relies on the correlation among the c...
متن کاملBayesian Error Based Sequences of Mutual Information Bounds
The inverse relation between mutual information (MI) and Bayesian error is sharpened by deriving finite sequences of upper and lower bounds on MI in terms of the minimum probability of error (MPE) and related Bayesian quantities. The well known Fano upper bound and Feder-Merhav lower bound on equivocation are tightened by including a succession of posterior probabilities starting at the largest...
متن کاملMutual Information Measures for Subclass Error-Correcting Output Codes Classification
Error-Correcting Output Codes (ECOCs) reveal a common way to model multi-class classification problems. According to this state of the art technique, a multi-class problem is decomposed into several binary ones. Additionally, on the ECOC framework we can apply the subclasses technique (sub-ECOC), where by splitting the initial classes of the problem we aim to the creation of larger but easier t...
متن کاملEstimating mutual information in high dimensions via classification error
Multivariate pattern analyses approaches in neuroimaging are fundamentally concerned with investigating the quantity and type of information processed by various regions of the human brain; typically, estimates of classification accuracy are used to quantify information. While a extensive and powerful library of methods can be applied to train and assess classifiers, it is not always clear how ...
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ژورنال
عنوان ژورنال: The Musical Times
سال: 1921
ISSN: 0027-4666
DOI: 10.2307/909153