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[null,"1.","Record Nr.",null,"UNISA990002082120203316"]
[null,null,"Autore",null,"CAIRD, James"]
[null,null,"Titolo",null,"English agriculture in 1850-51 / James Caird ; with a new introduction by G. E. Mingay"]
[null,null,"Pubbl/distr/stampa",null,"London : Frank Cass & Co., 1968"]
[null,null,"Descrizione fisica",null,"LII, 548 p. ; 23 cm"]
[null,null,"Disciplina",null,"630"]
[null,null,"Collocazione",null,"630 CAI 1 (ISE VI 56)"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"2.","Record Nr.",null,"UNINA9910627248403321"]
[null,null,"Autore",null,"Sevakula Rahul Kumar"]
[null,null,"Titolo",null,"Improving Classifier Generalization : Real-Time Machine Learning based Applications / / by Rahul Kumar Sevakula, Nishchal K. Verma"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023"]
[null,null,"ISBN",null,"981-19-5073-3"]
[null,null,"Edizione",null,"[1st ed. 2023.]"]
[null,null,"Descrizione fisica",null,"1 online resource (181 pages)"]
[null,null,"Collana",null,"Studies in Computational Intelligence, , 1860-9503 ; ; 989"]
[null,null,"Disciplina",null,"629.8"]
[null,null,"Soggetti",null,"Machine learning"]
[{"$nulls":4},"Computational intelligence"]
[{"$nulls":4},"Pattern recognition systems"]
[{"$nulls":4},"Machine Learning"]
[{"$nulls":4},"Computational Intelligence"]
[{"$nulls":4},"Automated Pattern Recognition"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Introduction to classification algorithms -- Methods to improve generalization performance -- MVPC – a classifier with very low VC dimension -- Framework for reliable fault detection with sensor data -- Membership functions for Fuzzy Support Vector Machine in noisy environment -- Stacked Denoising Sparse Autoencoder based Fuzzy rule classifiers -- Epilogue."]
[null,null,null,"Sommario/riassunto","This book elaborately discusses techniques commonly used to improve generalization performance in classification approaches. The contents highlight methods to improve classification performance in numerous case studies: ranging from datasets of UCI repository to predictive maintenance problems and cancer classification problems. The book specifically provides a detailed tutorial on how to approach time-series classification problems and discusses two real time case studies on condition monitoring. In addition to describing the various aspects a data scientist must consider before finalizing their approach to a classification problem and reviewing the state of the art for improving classification generalization performance, it also discusses in detail the authors own contributions to the field, including MVPC - a classifier with very low VC dimension, a graphical indices based framework for reliable predictive maintenance and a novel general-purpose membership functions for Fuzzy Support Vector Machine which "]
[{"$nulls":4},"provides state of the art performance with noisy datasets, and a novel scheme to introduce deep learning in Fuzzy Rule based classifiers (FRCs). This volume will serve as a useful reference for researchers and students working on machine learning, health monitoring, predictive maintenance, time-series analysis, gene-expression data classification. ."]