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Published by ISTE Ltd and John Wiley & Sons Inc, London, 2016
ISBN 10: 1848218133 ISBN 13: 9781848218130
Language: English
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Hardcover. Condition: new. Hardcover. Researchers and practitioners in food science and technology routinely face several challenges, related to sparseness and heterogeneity of data, as well as to the uncertainty in the measurements and the introduction of expert knowledge in the models. Evolutionary algorithms (EAs), stochastic optimization techniques loosely inspired by natural selection, can be effectively used to tackle these issues. In this book, we present a selection of case studies where EAs are adopted in real-world food applications, ranging from model learning to sensitivity analysis. Researchers and practitioners in food science and technology routinely face several challenges, related to sparseness and heterogeneity of data, as well as to the uncertainty in the measurements and the introduction of expert knowledge in the models. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Published by ISTE Ltd and John Wiley & Sons Inc, 2016
ISBN 10: 1848218133 ISBN 13: 9781848218130
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Add to basketHardback. Condition: New. New copy - Usually dispatched within 4 working days. 426.
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ISBN 10: 1848218133 ISBN 13: 9781848218130
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Published by ISTE Ltd and John Wiley & Sons Inc, 2016
ISBN 10: 1848218133 ISBN 13: 9781848218130
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Add to basketCondition: New. Researchers and practitioners in food science and technology routinely face several challenges, related to sparseness and heterogeneity of data, as well as to the uncertainty in the measurements and the introduction of expert knowledge in the models. Num Pages: 182 pages. BIC Classification: TDCT. Category: (P) Professional & Vocational. Dimension: 241 x 164 x 15. Weight in Grams: 426. . 2015. 1st Edition. Hardcover. . . . .
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ISBN 10: 1848218133 ISBN 13: 9781848218130
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Add to basketHardback. Condition: New. Researchers and practitioners in food science and technology routinely face several challenges, related to sparseness and heterogeneity of data, as well as to the uncertainty in the measurements and the introduction of expert knowledge in the models. Evolutionary algorithms (EAs), stochastic optimization techniques loosely inspired by natural selection, can be effectively used to tackle these issues. In this book, we present a selection of case studies where EAs are adopted in real-world food applications, ranging from model learning to sensitivity analysis.
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Add to basketGebunden. Condition: New. Researchers and practitioners in food science and technology routinely face several challenges, related to sparseness and heterogeneity of data, as well as to the uncertainty in the measurements and the introduction of expert knowledge in the models. Evolut.
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ISBN 10: 1848218133 ISBN 13: 9781848218130
Language: English
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Add to basketHardcover. Condition: Brand New. 1st edition. 182 pages. 9.25x6.25x0.75 inches. In Stock.
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Published by ISTE Ltd and John Wiley & Sons Inc, 2016
ISBN 10: 1848218133 ISBN 13: 9781848218130
Language: English
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Condition: New. Researchers and practitioners in food science and technology routinely face several challenges, related to sparseness and heterogeneity of data, as well as to the uncertainty in the measurements and the introduction of expert knowledge in the models. Num Pages: 182 pages. BIC Classification: TDCT. Category: (P) Professional & Vocational. Dimension: 241 x 164 x 15. Weight in Grams: 426. . 2015. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.
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Add to basketBuch. Condition: Neu. Neuware - Researchers and practitioners in food science and technology routinely face several challenges, related to sparseness and heterogeneity of data, as well as to the uncertainty in the measurements and the introduction of expert knowledge in the models. Evolutionary algorithms (EAs), stochastic optimization techniques loosely inspired by natural selection, can be effectively used to tackle these issues. In this book, we present a selection of case studies where EAs are adopted in real-world food applications, ranging from model learning to sensitivity analysis.
Published by ISTE Ltd and John Wiley and Sons Inc, GB, 2016
ISBN 10: 1848218133 ISBN 13: 9781848218130
Language: English
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Add to basketHardback. Condition: New. Researchers and practitioners in food science and technology routinely face several challenges, related to sparseness and heterogeneity of data, as well as to the uncertainty in the measurements and the introduction of expert knowledge in the models. Evolutionary algorithms (EAs), stochastic optimization techniques loosely inspired by natural selection, can be effectively used to tackle these issues. In this book, we present a selection of case studies where EAs are adopted in real-world food applications, ranging from model learning to sensitivity analysis.
Published by ISTE Ltd and John Wiley & Sons Inc, London, 2016
ISBN 10: 1848218133 ISBN 13: 9781848218130
Language: English
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Add to basketHardcover. Condition: new. Hardcover. Researchers and practitioners in food science and technology routinely face several challenges, related to sparseness and heterogeneity of data, as well as to the uncertainty in the measurements and the introduction of expert knowledge in the models. Evolutionary algorithms (EAs), stochastic optimization techniques loosely inspired by natural selection, can be effectively used to tackle these issues. In this book, we present a selection of case studies where EAs are adopted in real-world food applications, ranging from model learning to sensitivity analysis. Researchers and practitioners in food science and technology routinely face several challenges, related to sparseness and heterogeneity of data, as well as to the uncertainty in the measurements and the introduction of expert knowledge in the models. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.