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1. |
Record Nr. |
UNINA9910461828203321 |
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Autore |
Lottum Jelle von |
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Titolo |
Across the North Sea [[electronic resource] ] : the impact of the Dutch Republic on international labour migration, c. 1550-1850 / / Jelle van Lottum |
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Pubbl/distr/stampa |
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Amsterdam, : Aksant, 2007 |
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ISBN |
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1-283-25951-6 |
9786613259516 |
90-485-2107-6 |
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Descrizione fisica |
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1 online resource (252 p.) |
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Collana |
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Close encounters with the Dutch |
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Disciplina |
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Soggetti |
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Electronic books. |
Netherlands Emigration and immigration History |
North Sea Emigration and immigration History |
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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 and index. |
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Nota di contenuto |
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Across the North Sea; Contents; List of Figures; List of Tables; Acknowledgements; Introduction; 1 | Migrationin the NorthSea region,1550-1800: an assessment; 2 | The North Sea migration system; 3 | Comparing the two cores Migration to the Netherlands and England compared; 4 | Changing opportunities Labour market change and migration to the Netherlands; 5 | Two waves of mass migration Early modern and nineteenth century migration compared; Conclusions; Appendix I Estimate of the size of emigration in the North Sea region; Appendix II Six cross-sections of the Dutch maritime labour market |
Appendix III Estimate of the foreign workforce in the different branches of the Dutch maritime labour marketAppendix IV Urbanization in the North Sea region,1600-1800; Summary in Dutch; Bibliography; Index |
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Sommario/riassunto |
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Daily life in the early modern North Sea region was largely subject to international forces. International developments like wars, trade and changing religion trickled through all layers of society, and almost everyone enjoyed or suffered from the consequences. People, however, also came in direct contact with the outer world: they moved to another country, and did so in great numbers. The centre of attention for most |
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international migrants from the North Sea region was the Dutch Republic. From 1550 to 1800 this small confederation of provinces attracted hundreds of thousands of foreigners to |
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2. |
Record Nr. |
UNINA9910139455203321 |
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Autore |
Kulkarni Sanjeev |
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Titolo |
An elementary introduction to statistical learning theory [[electronic resource] /] / Sanjeev Kulkarni, Gilbert Harman |
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Pubbl/distr/stampa |
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Hoboken, N.J., : Wiley, c2011 |
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ISBN |
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1-283-09868-7 |
9786613098689 |
1-118-02346-3 |
1-118-02347-1 |
1-118-02343-9 |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (235 p.) |
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Collana |
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Wiley series in probability and statistics |
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Classificazione |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Machine learning - Statistical methods |
Pattern recognition systems |
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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 and index. |
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Nota di contenuto |
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An Elementary Introduction to Statistical Learning Theory; Contents; Preface; 1 Introduction: Classification, Learning, Features, and Applications; 1.1 Scope; 1.2 Why Machine Learning?; 1.3 Some Applications; 1.3.1 Image Recognition; 1.3.2 Speech Recognition; 1.3.3 Medical Diagnosis; 1.3.4 Statistical Arbitrage; 1.4 Measurements, Features, and Feature Vectors; 1.5 The Need for Probability; 1.6 Supervised Learning; 1.7 Summary; 1.8 Appendix: Induction; 1.9 Questions; 1.10 References; 2 Probability; 2.1 Probability of Some Basic Events; 2.2 Probabilities of Compound Events |
2.3 Conditional Probability2.4 Drawing Without Replacement; 2.5 A Classic Birthday Problem; 2.6 Random Variables; 2.7 Expected Value; 2.8 Variance; 2.9 Summary; 2.10 Appendix: Interpretations of |
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Probability; 2.11 Questions; 2.12 References; 3 Probability Densities; 3.1 An Example in Two Dimensions; 3.2 Random Numbers in [0,1]; 3.3 Density Functions; 3.4 Probability Densities in Higher Dimensions; 3.5 Joint and Conditional Densities; 3.6 Expected Value and Variance; 3.7 Laws of Large Numbers; 3.8 Summary; 3.9 Appendix: Measurability; 3.10 Questions; 3.11 References |
4 The Pattern Recognition Problem4.1 A Simple Example; 4.2 Decision Rules; 4.3 Success Criterion; 4.4 The Best Classifier: Bayes Decision Rule; 4.5 Continuous Features and Densities; 4.6 Summary; 4.7 Appendix: Uncountably Many; 4.8 Questions; 4.9 References; 5 The Optimal Bayes Decision Rule; 5.1 Bayes Theorem; 5.2 Bayes Decision Rule; 5.3 Optimality and Some Comments; 5.4 An Example; 5.5 Bayes Theorem and Decision Rule with Densities; 5.6 Summary; 5.7 Appendix: Defining Conditional Probability; 5.8 Questions; 5.9 References; 6 Learning from Examples; 6.1 Lack of Knowledge of Distributions |
6.2 Training Data6.3 Assumptions on the Training Data; 6.4 A Brute Force Approach to Learning; 6.5 Curse of Dimensionality, Inductive Bias, and No Free Lunch; 6.6 Summary; 6.7 Appendix: What Sort of Learning?; 6.8 Questions; 6.9 References; 7 The Nearest Neighbor Rule; 7.1 The Nearest Neighbor Rule; 7.2 Performance of the Nearest Neighbor Rule; 7.3 Intuition and Proof Sketch of Performance; 7.4 Using more Neighbors; 7.5 Summary; 7.6 Appendix: When People use Nearest Neighbor Reasoning; 7.6.1 Who Is a Bachelor?; 7.6.2 Legal Reasoning; 7.6.3 Moral Reasoning; 7.7 Questions; 7.8 References |
8 Kernel Rules8.1 Motivation; 8.2 A Variation on Nearest Neighbor Rules; 8.3 Kernel Rules; 8.4 Universal Consistency of Kernel Rules; 8.5 Potential Functions; 8.6 More General Kernels; 8.7 Summary; 8.8 Appendix: Kernels, Similarity, and Features; 8.9 Questions; 8.10 References; 9 Neural Networks: Perceptrons; 9.1 Multilayer Feedforward Networks; 9.2 Neural Networks for Learning and Classification; 9.3 Perceptrons; 9.3.1 Threshold; 9.4 Learning Rule for Perceptrons; 9.5 Representational Capabilities of Perceptrons; 9.6 Summary; 9.7 Appendix: Models of Mind; 9.8 Questions; 9.9 References |
10 Multilayer Networks |
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Sommario/riassunto |
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A thought-provoking look at statistical learning theory and its role in understanding human learning and inductive reasoning A joint endeavor from leading researchers in the fields of philosophy and electrical engineering, An Elementary Introduction to Statistical Learning Theory is a comprehensive and accessible primer on the rapidly evolving fields of statistical pattern recognition and statistical learning theory. Explaining these areas at a level and in a way that is not often found in other books on the topic, the authors present the basic theory behind contemporary ma |
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