Combining classifiers is a useful method for solving complex problems in machine learning. The ECOC (Error Correcting Output Codes) method has been widely used for designing combining classifiers with an emphasis on the diversity of classifiers. In this paper, in contrast to the standard ECOC approach in which individual classifiers are chosen homogeneously, classifiers are selected according to the complexity of the corresponding binary problem. We use SATIMAGE database (containing 6 classes) for our experiments. The recognition error rate in our proposed method is %10.37 which indicates a considerable improvement in comparison with the conventional ECOC and stack generalization methods
Error Correcting Output Coding (ECOC) methods for multiclass classification present several open pro...
Error correcting output coding is a well known technique to decompose a multi-class classification p...
Error correcting output coding is a well known technique to decompose a multi-class classification p...
In "decomposition/reconstruction" strategy, we can solve a complex problem by 1) decomposing the pro...
In "decomposition/reconstruction" strategy, we can solve a complex problem by 1) decomposing the pro...
Multiclass classification is a fundamental and challenging task in machine learning. The existing te...
Error Correcting Output Coding (ECOC) is a multiclass classification technique, in which multiple ba...
Error Correcting Output Coding (ECOC) is a multiclass classification technique, in which multiple ba...
Traditional artificial neural architectures possess limited ability to address the scale problem exh...
There is a trend in recent OCR development to improve system performance by combining recognition re...
In the framework of decomposition methods for multiclass classification problems, error correcting o...
Though deep learning has been applied successfully in many scenarios, malicious inputs with human-im...
Error correcting output coding is a well known technique to decompose a multi-class classification p...
Error correcting output coding is a well known technique to decompose a multi-class classification p...
Error correcting output coding is a well known technique to decompose a multi-class classification p...
Error Correcting Output Coding (ECOC) methods for multiclass classification present several open pro...
Error correcting output coding is a well known technique to decompose a multi-class classification p...
Error correcting output coding is a well known technique to decompose a multi-class classification p...
In "decomposition/reconstruction" strategy, we can solve a complex problem by 1) decomposing the pro...
In "decomposition/reconstruction" strategy, we can solve a complex problem by 1) decomposing the pro...
Multiclass classification is a fundamental and challenging task in machine learning. The existing te...
Error Correcting Output Coding (ECOC) is a multiclass classification technique, in which multiple ba...
Error Correcting Output Coding (ECOC) is a multiclass classification technique, in which multiple ba...
Traditional artificial neural architectures possess limited ability to address the scale problem exh...
There is a trend in recent OCR development to improve system performance by combining recognition re...
In the framework of decomposition methods for multiclass classification problems, error correcting o...
Though deep learning has been applied successfully in many scenarios, malicious inputs with human-im...
Error correcting output coding is a well known technique to decompose a multi-class classification p...
Error correcting output coding is a well known technique to decompose a multi-class classification p...
Error correcting output coding is a well known technique to decompose a multi-class classification p...
Error Correcting Output Coding (ECOC) methods for multiclass classification present several open pro...
Error correcting output coding is a well known technique to decompose a multi-class classification p...
Error correcting output coding is a well known technique to decompose a multi-class classification p...