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1. |
Record Nr. |
UNISA990002285220203316 |
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Autore |
ONADO, Marco |
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Titolo |
Banca e sistema finanziario / Marco Onado |
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Pubbl/distr/stampa |
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Bologna : Il Mulino, 1988 |
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Descrizione fisica |
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Collana |
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La nuova scienza , Serie di economia |
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Disciplina |
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Soggetti |
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Collocazione |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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2. |
Record Nr. |
UNINA9910782214403321 |
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Autore |
Killick Tim |
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Titolo |
British short fiction in the early nineteenth century [[electronic resource] ] : the rise of the tale / / Tim Killick |
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Pubbl/distr/stampa |
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Aldershot, England ; ; Burlington, VT, : Ashgate, c2008 |
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ISBN |
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1-315-57029-7 |
1-317-17146-2 |
1-317-17145-4 |
1-281-79858-4 |
9786611798581 |
0-7546-8212-9 |
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Descrizione fisica |
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1 online resource (200 p.) |
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Disciplina |
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Soggetti |
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English fiction - 19th century - History and criticism |
Literary form - History - 19th century |
Short stories, English - History and criticism |
Short story |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references (p. [165]-187) and index. |
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Nota di contenuto |
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Cover; Contents; Acknowledgements; Introduction; 1 Overview: Short Fiction in the Early Nineteenth Century; 2 Washington Irving: Geoffrey Crayon and the Market for Short Fiction; 3 Improving Stories: Women Writers, Morality, and Short Fiction; 4 Regionalism and Folklore: Local Stories and Traditional Forms; Conclusion: Short Fiction in the 1830's; Bibliography; Index |
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Sommario/riassunto |
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Contextualizing British short fiction within the broader context of Romantic-era print culture, Tim Killick argues that authors such as Washington Irving, Mary Russell Mitford, and James Hogg championed the use of short fiction during a period predominantly associated with novel-writing and poetry. His book makes a convincing case for the evolution of short fiction into a self-conscious and modern genre, with its own techniques and imperatives, separate from those of the novel. |
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3. |
Record Nr. |
UNINA9910896181303321 |
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Autore |
Franke Jürgen |
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Titolo |
Statistical Machine Learning for Engineering with Applications / / edited by Jürgen Franke, Anita Schöbel |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
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ISBN |
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Edizione |
[1st ed. 2024.] |
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Descrizione fisica |
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1 online resource (393 pages) |
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Collana |
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Lecture Notes in Statistics, , 2197-7186 ; ; 227 |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Statistics |
Machine learning |
Statistical Theory and Methods |
Machine Learning |
Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences |
Aprenentatge automàtic |
Estadística |
Llibres electrònics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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- An Introduction of Statistical Learning for Engineers -- Machine Learning for Inline Surface Inspection Systems - Challenges, Approaches, and Application Example -- Gaussian Process Regression for the Prediction of Cable Bundle Characteristics -- Machine Learning for Predictive Maintenance in Production Environments -- Detecting Healthcare Fraud Using Hybrid Machine Learning for Document Digitization -- Cracks in concrete -- Machine learning methods for prediction of breakthrough curves in reactive porous media -- Segmentation and Aggregation in Text Classification -- Hardware-aware Neural Architecture Search -- Optimal Experimental Design Supported by Machine Learning Regression Models -- Data Analytics, Artificial Intelligence and Machine Learning in Mobility and Vehicle Engineering. |
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Sommario/riassunto |
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This book offers a leisurely introduction to the concepts and methods of machine learning. Readers will learn about classification trees, Bayesian learning, neural networks and deep learning, the design of experiments, and related methods. For ease of reading, technical details are avoided as far as possible, and there is a particular emphasis on applicability, interpretation, reliability and limitations of the data-analytic methods in practice. To cover the common availability and types of data in engineering, training sets consisting of independent as well as time series data are considered. To cope with the scarceness of data in industrial problems, augmentation of training sets by additional artificial data, generated from physical models, as well as the combination of machine learning and expert knowledge of engineers are discussed. The methodological exposition is accompanied by several detailed case studies based on industrial projects covering a broad range of engineering applications from vehicle manufacturing, process engineering and design of materials to optimization of production processes based on image analysis. The focus is on fundamental ideas, applicability and the pitfalls of machine learning in industry and science, where data are often scarce. Requiring only very basic background in statistics, the book is ideal for self-study or short courses for engineering and science students. |
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