Carlo I d'Angio e il Mediterraneo : politica, diplomazia e commercio internazionale prima dei Vespri / Gian Luca Borghese

• Record Nr.: UNISA990003435100203316
• Autore: BORGHESE, Gian Luca
• Pubbl/distr/stampa: Roma, : École française de Rome, 2008
• ISBN: 978-2-7283-0827-9
• Descrizione fisica: 336 p. : ill. ; 28 cm + CD ROM
• Collana: Collection de l'École française de Rome ; 411
• Disciplina: 945.7052092
• Soggetti: Carlo : d'Angio <re di Sicilia ; 1.>
• Collocazione: X.1.B. 1317
• Lingua di pubblicazione: Italiano
• Formato: Materiale a stampa
• Livello bibliografico: Monografia

Latinx Literature Unbound : Undoing Ethnic Expectation / / Ralph E. Rodriguez

• Record Nr.: UNINA9910480689903321
• Autore: Rodriguez Ralph E.
• Pubbl/distr/stampa: New York, NY : , : Fordham University Press, , [2018]; ©2018
• ISBN: 0-8232-8144-2; 0-8232-7925-1; 0-8232-7941-3
• Edizione: [First edition.]
• Descrizione fisica: 1 online resource (1 PDF (181 pages))
• Disciplina: 860.9868073
• Soggetti: Hispanic American authors; Hispanic American literature (Spanish) - History and criticism; American literature - Hispanic American authors - History and criticism; Electronic books.
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: This edition previously issued in print: 2018.
• Nota di bibliografia: Includes bibliographical references (pages 161-173) and index.
• Nota di contenuto: Front matter -- Contents -- Introduction. What We Talk about When We Talk about Latinx Literature -- Chapter 1. Brown Like Me? The Author- Function, Proper Names, and the Rise of Fictional Nobodies -- Chapter 2. Confounding the Mimetic: The Metafictional Challenge to Representation -- Chapter 3. From Where I Stand: The Intimacy and Distance of We and You in the Short Story -- Chapter 4. The Lyric, or, a Radical Singularity in Latinx Verse -- Conclusion: Thinking beyond Limits -- Acknowledgments -- Notes -- works cited -- Index
• Sommario/riassunto: Since the 1990's, there has been unparalleled growth in the literary output from an ever more diverse group of Latinx writers. Extant criticism, however, has yet to catch up with the diversity of writers we label Latinx and the range of themes about which they write. Little sustained scholarly attention has been paid, moreover, to the very category under which we group this literature. Latinx Literature Unbound, thus, begins with a fundamental question “What does it mean to label a work of literature or an entire corpus of literature Latinx?” From this question others emerge: What does Latinx allow or ; predispose us to see, and what does it preclude us from seeing? If the grouping—which brings together a heterogeneous collection of people under a seemingly homogeneous label—tells us something meaningful, is there a poetics we can develop that would facilitate our analysis of this literature? In answering these questions, Latinx Literature Unbound frees Latinx literature from taken-for-granted critical assumptions about identity and theme. It argues that there may be more salubrious taxonomies than Latinx for organizing and analyzing this literature. Privileging the act of reading as a temporal, meaning-making event, Ralph E. Rodriguez argues that genre may be a more durable category for analyzing this literature and suggests new ways we might proceed with future studies of the writing we have come to identify as Latinx.

Self-adaptive systems for machine intelligence / / Haibo He

• Record Nr.: UNINA9911019424903321
• Autore: He Haibo <1976->
• Pubbl/distr/stampa: Hoboken, N.J., : Wiley-Interscience, 2011
• ISBN: 9786613175694; 9781283175692; 128317569X; 9781118025598; 1118025598; 9781118025604; 1118025601; 9781118025581; 111802558X
• Descrizione fisica: 1 online resource (248 p.)
• Classificazione: COM044000
• Disciplina: 006.3/1
• Soggetti: Machine learning; Self-organizing systems; Artificial intelligence
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: SELF-ADAPTIVE SYSTEMS FOR MACHINE INTELLIGENCE; CONTENTS; Preface; Acknowledgments; 1 Introduction; 1.1 The Machine Intelligence Research; 1.2 The Two-Fold Objectives: Data-Driven and Biologically Inspired Approaches; 1.3 How to Read This Book; 1.3.1 Part I: Data-Driven Approaches for Machine Intelligence (Chapters 2, 3, and 4); 1.3.2 Part II: Biologically-Inspired Approaches for Machine Intelligence (Chapters 4, 5, and 6); 1.4 Summary and Further Reading; References; 2 Incremental Learning; 2.1 Introduction; 2.2 Problem Foundation; 2.3 An Adaptive Incremental Learning Framework; 2.4 Design of the Mapping Function2.4.1 Mapping Function Based on Euclidean Distance; 2.4.2 Mapping Function Based on Regression Learning Model; 2.4.3 Mapping Function Based on Online Value System; 2.4.3.1 A Three-Curve Fitting (TCF) Technique; 2.4.3.2 System-Level Architecture for Online Value Estimation; 2.5 Case Study; 2.5.1 Incremental Learning from Video Stream; 2.5.1.1 Feature Representation; 2.5.1.2 Experimental Results; 2.5.1.3 Concept Drifting Issue in Incremental Learning; 2.5.2 Incremental Learning for Spam E-mail Classification; 2.5.2.1 Data Set Characteristic and System Configuration2.5.2.2 Simulation Results; 2.6 Summary; References; 3 Imbalanced Learning; 3.1 Introduction; 3.2 The Nature of Imbalanced Learning; 3.3 Solutions for Imbalanced Learning; 3.3.1 Sampling Methods for Imbalanced Learning; 3.3.1.1 Random Oversampling and Undersampling; 3.3.1.2 Informed Undersampling; 3.3.1.3 Synthetic Sampling with Data Generation; 3.3.1.4 Adaptive Synthetic Sampling; 3.3.1.5 Sampling with Data Cleaning Techniques; 3.3.1.6 Cluster-Based Sampling Method; 3.3.1.7 Integration of Sampling and Boosting; 3.3.2 Cost-Sensitive Methods for Imbalanced Learning3.3.2.1 Cost-Sensitive Learning Framework; 3.3.2.2 Cost-Sensitive Data Space Weighting with Adaptive Boosting; 3.3.2.3 Cost-Sensitive Decision Trees; 3.3.2.4 Cost-Sensitive Neural Networks; 3.3.3 Kernel-Based Methods for Imbalanced Learning; 3.3.3.1 Kernel-Based Learning Framework; 3.3.3.2 Integration of Kernel Methods with Sampling Methods; 3.3.3.3 Kernel Modification Methods for Imbalanced Learning; 3.3.4 Active Learning Methods for Imbalanced Learning; 3.3.5 Additional Methods for Imbalanced Learning; 3.4 Assessment Metrics for Imbalanced Learning3.4.1 Singular Assessment Metrics; 3.4.2 Receiver Operating Characteristics (ROC) Curves; 3.4.3 Precision-Recall (PR) Curves; 3.4.4 Cost Curves; 3.4.5 Assessment Metrics for Multiclass Imbalanced Learning; 3.5 Opportunities and Challenges; 3.6 Case Study; 3.6.1 Nonlinear Normalization; 3.6.2 Data Sets Distribution; 3.6.3 Simulation Results and Discussions; 3.7 Summary; References; 4 Ensemble Learning; 4.1 Introduction; 4.2 Hypothesis Diversity; 4.2.1 Q-Statistics; 4.2.2 Correlation Coefficient; 4.2.3 Disagreement Measure; 4.2.4 Double-Fault Measure
• Sommario/riassunto: "This book will advance the understanding and application of self-adaptive intelligent systems; therefore it will potentially benefit the long-term goal of replicating certain levels of brain-like intelligence in complex and networked engineering systems. It will provide new approaches for adaptive systems within uncertain environments. This will provide an opportunity to evaluate the strengths and weaknesses of the current state-of-the-art of knowledge, give rise to new research directions, and educate future professionals in this domain. Self-adaptive intelligent systems have wide applications from military security systems to civilian daily life. In this book, different application problems, including pattern recognition, classification, image recovery, ; and sequence learning, will be presented to show the capability of the proposed systems in learning, memory, and prediction. Therefore, this book will also provide potential new solutions to many real-world applications"--; "This book will advance the understanding and application of self-adaptive intelligent systems; therefore it will potentially benefit the long-term goal of replicating certain levels of brain-like intelligence in complex and networked engineering systems. It will provide new approaches for adaptive systems within uncertain environments. This will provide an opportunity to evaluate the strengths and weaknesses of the current state-of-the-art of knowledge, give rise to new research directions, and educate future professionals in this domain"--