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Record Nr. |
UNINA9910463666003321 |
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Autore |
McPherson James M. |
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Titolo |
The struggle for equality : abolitionists and the negro in the Civil War and reconstruction / / by James M. McPherson |
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Pubbl/distr/stampa |
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Princeton, New Jersey : , : Princeton University Press, , 2014 |
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©2014 |
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ISBN |
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0-691-04566-6 |
1-4008-5223-4 |
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Edizione |
[Updated edition with a New Preface] |
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Descrizione fisica |
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1 online resource (874 p.) |
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Collana |
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Princeton Classics ; ; 12 |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Abolitionists |
African Americans - History - 1863-1877 |
Slaves - Emancipation - United States |
Electronic books. |
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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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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Front matter -- CONTENTS -- PREFACE TO THE PRINCETON CLASSICS EDITION -- PREFACE -- KEY TO ABBREVIATIONS -- INTRODUCTION -- I. THE ELECTION OF 1860 -- II. SECESSION AND THE COMING OF WAR -- III. THE EMANCIPATION ISSUE: 1861 -- IV. EMANCIPATION AND PUBLIC OPINION: 1861-1862 -- V. THE EMANCIPATION PROCLAMATION AND THE THIRTEENTH AMENDMENT -- VI. THE NEGRO: INNATELY INFERIOR OR EQUAL? -- VII. FREEDMEN'S EDUCATION, 1861-1865 -- VIII. THE CREATION OF THE FREEDMEN'S BUREAU -- IX. MEN OF COLOR , TO ARMS! -- X. THE QUEST FOR EQUAL RIGHTS IN THE NORTH -- XI. THE BALLOT AND LAND FOR THE FREEDMEN: 1861-1865 -- XII. THE REELECTION OF LINCOLN -- XIII. SCHISM IN THE RANKS: 1864-1865 -- XIV. ANDREW JOHNSON AND RECONSTRUCTION: 1865 -- XV. THE FOURTEENTH AMENDMENT AND THE ELECTION OF 1866 -- XVI. MILITARY RECONSTRUCTION AND IMPEACHMENT -- XVII. EDUCATION AND CONFISCATION: 1865-1870 -- XVIII. THE CLIMAX OF THE CRUSADE: THE FIFTEENTH AMENDMENT -- BIBLIOGRAPHICAL ESSAY -- INDEX |
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Sommario/riassunto |
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Originally published in 1964, The Struggle for Equality presents an |
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incisive and vivid look at the abolitionist movement and the legal basis it provided to the civil rights movement of the 1960's. Pulitzer Prize-winning historian James McPherson explores the role played by rights activists during and after the Civil War, and their evolution from despised fanatics into influential spokespersons for the radical wing of the Republican Party. Asserting that it was not the abolitionists who failed to instill principles of equality, but rather the American people who refused to follow their leadership, McPherson raises questions about the obstacles that have long hindered American reform movements. This new Princeton Classics edition marks the fiftieth anniversary of the book's initial publication and includes a new preface by the author. |
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2. |
Record Nr. |
UNINA9910830752003321 |
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Titolo |
Kalman filtering and neural networks [[electronic resource] /] / edited by Simon Haykin |
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Pubbl/distr/stampa |
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ISBN |
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1-280-36756-3 |
9786610367566 |
0-470-31226-2 |
0-471-46421-X |
0-471-22154-6 |
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Descrizione fisica |
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1 online resource (302 p.) |
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Collana |
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Adaptive and learning systems for signal processing, communications, and control |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Kalman filtering |
Neural networks (Computer science) |
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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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KALMAN FILTERING AND NEURAL NETWORKS; CONTENTS; Preface; Contributors; 1 Kalman Filters; 1.1 Introduction; 1.2 Optimum |
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Estimates; 1.3 Kalman Filter; 1.4 Divergence Phenomenon: Square-Root Filtering; 1.5 Rauch-Tung-Striebel Smoother; 1.6 Extended Kalman Filter; 1.7 Summary; References; 2 Parameter-Based Kalman Filter Training: Theory and Implementation; 2.1 Introduction; 2.2 Network Architectures; 2.3 The EKF Procedure; 2.3.1 Global EKF Training; 2.3.2 Learning Rate and Scaled Cost Function; 2.3.3 Parameter Settings; 2.4 Decoupled EKF (DEKF); 2.5 Multistream Training |
2.5.1 Some Insight into the Multistream Technique2.5.2 Advantages and Extensions of Multistream Training; 2.6 Computational Considerations; 2.6.1 Derivative Calculations; 2.6.2 Computationally Efficient Formulations for Multiple-Output Problems; 2.6.3 Avoiding Matrix Inversions; 2.6.4 Square-Root Filtering; 2.7 Other Extensions and Enhancements; 2.7.1 EKF Training with Constrained Weights; 2.7.2 EKF Training with an Entropic Cost Function; 2.7.3 EKF Training with Scalar Errors; 2.8 Automotive Applications of EKF Training; 2.8.1 Air/Fuel Ratio Control; 2.8.2 Idle Speed Control |
2.8.3 Sensor-Catalyst Modeling2.8.4 Engine Misfire Detection; 2.8.5 Vehicle Emissions Estimation; 2.9 Discussion; 2.9.1 Virtues of EKF Training; 2.9.2 Limitations of EKF Training; 2.9.3 Guidelines for Implementation and Use; References; 3 Learning Shape and Motion from Image Sequences; 3.1 Introduction; 3.2 Neurobiological and Perceptual Foundations of our Model; 3.3 Network Description; 3.4 Experiment 1; 3.5 Experiment 2; 3.6 Experiment 3; 3.7 Discussion; References; 4 Chaotic Dynamics; 4.1 Introduction; 4.2 Chaotic (Dynamic) Invariants; 4.3 Dynamic Reconstruction |
4.4 Modeling Numerically Generated Chaotic Time Series4.4.1 Logistic Map; 4.4.2 Ikeda Map; 4.4.3 Lorenz Attractor; 4.5 Nonlinear Dynamic Modeling of Real-World Time Series; 4.5.1 Laser Intensity Pulsations; 4.5.2 Sea Clutter Data; 4.6 Discussion; References; 5 Dual Extended Kalman Filter Methods; 5.1 Introduction; 5.2 Dual EKF-Prediction Error; 5.2.1 EKF-State Estimation; 5.2.2 EKF-Weight Estimation; 5.2.3 Dual Estimation; 5.3 A Probabilistic Perspective; 5.3.1 Joint Estimation Methods; 5.3.2 Marginal Estimation Methods; 5.3.3 Dual EKF Algorithms; 5.3.4 Joint EKF |
5.4 Dual EKF Variance Estimation5.5 Applications; 5.5.1 Noisy Time-Series Estimation and Prediction; 5.5.2 Economic Forecasting-Index of Industrial Production; 5.5.3 Speech Enhancement; 5.6 Conclusions; Acknowledgments; Appendix A: Recurrent Derivative of the Kalman Gain; Appendix B: Dual EKF with Colored Measurement Noise; References; 6 Learning Nonlinear Dynamical System Using the Expectation-Maximization Algorithm; 6.1 Learning Stochastic Nonlinear Dynamics; 6.1.1 State Inference and Model Learning; 6.1.2 The Kalman Filter; 6.1.3 The EM Algorithm; 6.2 Combining EKS and EM |
6.2.1 Extended Kalman Smoothing (E-step) |
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Sommario/riassunto |
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State-of-the-art coverage of Kalman filter methods for the design of neural networks This self-contained book consists of seven chapters by expert contributors that discuss Kalman filtering as applied to the training and use of neural networks. Although the traditional approach to the subject is almost always linear, this book recognizes and deals with the fact that real problems are most often nonlinear. The first chapter offers an introductory treatment of Kalman filters with an emphasis on basic Kalman filter theory, Rauch-Tung-Striebel smoother, and the extended Kalman filter. O |
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