Add to ORKGThe main training objective of the learning object is to introduce some of the most popular estimators for classification error: resubstitution, holdout, K-fold cross validation, bootstrap and repeated variants for holdout and K-fold cross validation. They are described at a basic level and then compared in terms of bias and variability.https://polimedia.upv.es/visor/?id=108cee50-70af-11e9-a7d3-3df1cef1857dJuan Císcar, A.; Sanchis Navarro, JA.; Civera Saiz, J. (2019). Error estimation in pattern recognition. http://hdl.handle.net/10251/12129
[[abstract]]The authors report results on the application of several bootstrap techniques in estimat...
this paper we investigate several ways of utilizing error-dependent resampling for artificial neural...
Discrete Classification problems abound in pattern recognition and data mining applications. One of ...
This book is the first of its kind to discuss error estimation with a model-based approach. From the...
Classification in bioinformatics often suffers from small samples in conjunction with large numbers ...
In the machine learning field the performance of a classifier is usually measured in terms of predic...
We study the notions of bias and variance for classification rules. Following Efron (1978) we develo...
Several methods (independent subsamples, leave-one-out, cross-validation, and bootstrapping) have be...
Several methods (independent subsamples, leave-one-out, cross-validation, and bootstrapping) have be...
Several methods (independent subsamples, leave-one-out, cross-validation, and bootstrapping) have be...
Several methods (independent subsamples, leave-one-out, cross-validation, and bootstrapping) have be...
Since the training error tends to underestimate the true test error, an appropriate test error estim...
We propose a general method for error estimation that displays low variance and gen-erally low bias ...
Classification is an important branch of machine learning that impacts many areas of modern life. Ma...
In genomic studies, thousands of features are collected on relatively few samples. One of the goals ...
[[abstract]]The authors report results on the application of several bootstrap techniques in estimat...
this paper we investigate several ways of utilizing error-dependent resampling for artificial neural...
Discrete Classification problems abound in pattern recognition and data mining applications. One of ...
This book is the first of its kind to discuss error estimation with a model-based approach. From the...
Classification in bioinformatics often suffers from small samples in conjunction with large numbers ...
In the machine learning field the performance of a classifier is usually measured in terms of predic...
We study the notions of bias and variance for classification rules. Following Efron (1978) we develo...
Several methods (independent subsamples, leave-one-out, cross-validation, and bootstrapping) have be...
Several methods (independent subsamples, leave-one-out, cross-validation, and bootstrapping) have be...
Several methods (independent subsamples, leave-one-out, cross-validation, and bootstrapping) have be...
Several methods (independent subsamples, leave-one-out, cross-validation, and bootstrapping) have be...
Since the training error tends to underestimate the true test error, an appropriate test error estim...
We propose a general method for error estimation that displays low variance and gen-erally low bias ...
Classification is an important branch of machine learning that impacts many areas of modern life. Ma...
In genomic studies, thousands of features are collected on relatively few samples. One of the goals ...
[[abstract]]The authors report results on the application of several bootstrap techniques in estimat...
this paper we investigate several ways of utilizing error-dependent resampling for artificial neural...
Discrete Classification problems abound in pattern recognition and data mining applications. One of ...