1,721,117 research outputs found
NEURObjects: an object-oriented library for neural network development
No full textNEURObjects is a set of C++ library classes for neural network development, exploiting the potentialities of object-oriented design and programming. The main goal of the library consists in supporting experimental research in neural networks and fast prototyping of inductive machine learning applications. We present NEURObjects design issues, its main functionalities, and programming examples, showing how to map neural network concepts into the design of library classes
Effectiveness of error correcting output coding methods in ensemble and monolithic learning machines
No full textError Correcting Output Coding (ECOC) methods for multiclass classification present several open problems ranging from the trade-off between their error recovering capabilities and the learnability of the induced dichotomies to the selection of proper base learners and to the design of well-separated codes for a given multiclass problem. We experimentally analyse some of the main factors affecting the effectiveness of ECOC methods. We show that the architecture of ECOC learning machines influences the accuracy of the ECOC classifier, highlighting that ensembles of parallel and independent dichotomic Multi-Layer Perceptrons are well-suited to implement ECOC methods. We quantitatively evaluate the dependence among codeword bit errors using mutual information based measures, experimentally showing that a low dependence enhances the generalisation capabilities of ECOC. Moreover we show that the proper selection of the base learner and the decoding function of the reconstruction stage significantly affects the performance of the ECOC ensemble. The analysis of the relationships between the error recovering power, the accuracy of the base learners, and the dependence among codeword bits show that all these factors concur to the effectiveness of ECOC methods in a not straightforward way, very likely dependent on the distribution and complexity of the data
An experimental analysis of the dependence among codeword bit errors in ECOC learning machines
No full textOne of the main factors affecting the effectiveness of Error Correcting Output Coding (ECOC) methods for classification is the dependence among the errors of the computed codeword bits.
We present an extensive experimental work for evaluating the dependence among output errors of the decomposition unit in ECOC learning machines.
In particular, we apply measures based on mutual information to compare the dependence of ECOC Multi-Layer Perceptron (ECOC MLP), made up by a single multi-input multi-output MLP, and ECOC ensembles made up by a set of independent and parallel dichotomizers (ECOC PND).
Moreover, the experimentation analyzes the relationship between the architecture, the dependence among output errors and the performances of ECOC learning machines.
The results show that the dependence among computed codeword bits is significantly smaller for ECOC PND, pointing out that ensembles of independent parallel dichotomizers are better suited for implementing ECOC classification methods.
The experimental results suggest new architectures of ECOC learning machines to
improve the independence among output errors and the diversity between base learners
Quantitative evaluation of dependence among outputs in ECOC classifiers using mutual information based measures
No full textIn previous works, it has been experimentally shown that the implementation of Error Correcting Output Coding (ECOC) classification methods with an ensemble of parallel and independent non linear dichotomizers (ECOC PND) outperforms the implementation with a single monolithic multi layer perceptron (ECOC MLP). The low dependence of the errors on different codeword bits was qualitatively indicated as one of the main factors affecting this result. In this paper, we quantitatively evaluate the dependence of output errors in ECOC learning machines using mutual information based measures, and we study the relation between dependence of output errors and classification performances
Soft Computing Applications (Advances in Intelligent and Soft Computing)
No full textThe papers collected in this book are concerned with the application of the so-called "soft-computing" techniques to the aim of defining flexible systems. The topics covered witness the actual research trend towards an integration of distinct formal techniques for defining flexible systems. The contributions in this volume mainly concern the definition of systems in several application fields, such as image processing, control, asset allocation, medicine, time series forecasting, qualitative modeling, support to design, reliability analysis, diagnosis, filtering, data analysis, land mines detection and so forth. The papers presented in this volume are organized into three main thematic sections: Fuzzy Systems, Neural Networks and Genetic and Evolutionary Algorithms, although, as outlined before, some works employ more than one technique from these fields
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