A comparative study of the scalability of a sensitivity-based learning algorithm for artificial neural networks

► Researchers must now study not only accuracy but also scalability. ► Researchers are investigating machine learning scalability to large scale problems. ► The scalability of popular training algorithms for ANNs is analyzed in this research. ► The training algorithm SBLLM performs better than other...

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Veröffentlicht in:	Expert systems with applications Jg. 40; H. 10; S. 3900 - 3905
Hauptverfasser:	Peteiro-Barral, Diego, Guijarro-Berdiñas, Bertha, Pérez-Sánchez, Beatriz, Fontenla-Romero, Oscar
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier Ltd 01.08.2013 Elsevier
Schlagworte:	Algorithms Applied sciences Artificial intelligence Artificial neural networks Biological and medical sciences Classifier design and evaluation Computer science; control theory; systems Computer systems and distributed systems. User interface Connectionism. Neural networks Exact sciences and technology Fundamental and applied biological sciences. Psychology General aspects Learning Linear systems Machine learning Mathematical analysis Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) Memory and file management (including protection and security) Memory organisation. Data processing Neural nets Sensitivity analysis Software Training Algorithms Neural nets Classifier design and evaluation Machine learning Sensitivity analysis Scalability Intruder detector Neural network Modeling Optimization Learning (artificial intelligence) Classification Data field Feedforward Learning algorithm Bioinformatics Artificial intelligence Computer security Intrusion detection systems
ISSN:	0957-4174, 1873-6793
Online-Zugang:	Volltext
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