Optimization for machine learning / / edited by Suvrit Sra, Sebastian Nowozin, and Stephen J. Wright

• Record Nr.: UNINA9910964937403321
• Pubbl/distr/stampa: Cambridge, Mass., : MIT Press, c2012
• ISBN: 9786613302847; 9780262297899; 0262297892; 9781283302845; 1283302845
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (509 p.)
• Collana: Neural information processing series
• Altri autori (Persone): SraSuvrit <1976->; NowozinSebastian <1980->; WrightStephen J. <1960->
• Disciplina: 006.3/1
• Soggetti: Machine learning - Mathematical models; Mathematical optimization
• 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.
• Nota di contenuto: Contents; Series Foreword; Preface; Chapter 1. Introduction: Optimization and Machine Learning; 1.1 Support Vector Machines; 1.2 Regularized Optimization; 1.3 Summary of the Chapters; 1.4 References; Chapter 2. Convex Optimization with Sparsity-Inducing Norms; 2.1 Introduction; 2.2 Generic Methods; 2.3 Proximal Methods; 2.4 (Block) Coordinate Descent Algorithms; 2.5 Reweighted- 2 Algorithms; 2.6 Working-Set Methods; 2.7 Quantitative Evaluation; 2.8 Extensions; 2.9 Conclusion; 2.10 References; Chapter 3. Interior-Point Methods for Large-Scale Cone Programming; 3.1 Introduction; 3.2 Primal-Dual Interior-Point Methods3.3 Linear and Quadratic Programming; 3.4 Second-Order Cone Programming; 3.5 Semidefinite Programming; 3.6 Conclusion; 3.7 References; Chapter 4. Incremental Gradient, Subgradient, and Proximal Methods for Convex Optimization: A Survey; 4.1 Introduction; 4.2 Incremental Subgradient-Proximal Methods; 4.3 Convergence for Methods with Cyclic Order; 4.4 Convergence for Methods with Randomized Order; 4.5 Some ; Applications; 4.6 Conclusions; 4.7 References; Chapter 5. First-Order Methods for Nonsmooth Convex Large-Scale Optimization, I: General Purpose Methods; 5.1 Introduction5.2 Mirror Descent Algorithm: Minimizing over a Simple Set; 5.3 Problems with Functional Constraints; 5.4 Minimizing Strongly Convex Functions; 5.5 Mirror Descent Stochastic Approximation; 5.6 Mirror Descent for Convex-Concave Saddle-Point Problems; 5.7 Setting up a Mirror Descent Method; 5.8 Notes and Remarks; 5.9 References; Chapter 6. First-Order Methods for Nonsmooth Convex Large-Scale Optimization, II: Utilizing Problem's Structure; 6.1 Introduction; 6.2 Saddle-Point Reformulations of Convex Minimization Problems; 6.3 Mirror-Prox Algorithm; 6.4 Accelerating the Mirror-Prox Algorithm6.5 Accelerating First-Order Methods by Randomization; 6.6 Notes and Remarks; 6.7 References; Chapter 7. Cutting-Plane Methods in Machine Learning; 7.1 Introduction to Cutting-plane Methods; 7.2 Regularized Risk Minimization; 7.3 Multiple Kernel Learning; 7.4 MAP Inference in Graphical Models; 7.5 References; Chapter 8. Introduction to Dual Decomposition for Inference; 8.1 Introduction; 8.2 Motivating Applications; 8.3 Dual Decomposition and Lagrangian Relaxation; 8.4 Subgradient Algorithms; 8.6 Relations to Linear Programming Relaxations; 8.7 Decoding: Finding the MAP Assignment8.8 Discussion; Appendix: Technical Details; 8.10 References; 8.5 Block Coordinate Descent Algorithms; Chapter 9. Augmented Lagrangian Methods for Learning, Selecting, and Combining Features; 9.1 Introduction; 9.2 Background; 9.3 Proximal Minimization Algorithm; 9.4 Dual Augmented Lagrangian (DAL) Algorithm; 9.5 Connections; 9.6 Application; 9.7 Summary; Acknowledgment; Appendix: Mathematical Details; 9.9 References; Chapter 10. The Convex Optimization Approach to Regret Minimization; 10.1 Introduction; 10.2 The RFTL Algorithm and Its Analysis; 10.3 The "Primal-Dual" Approach
• Sommario/riassunto: An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities.The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields.Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and ; within the broader optimization community.

Philosophy, literature, and politics : essays honoring Ellis Sandoz / / edited by Charles R. Embry and Barry Cooper

• Record Nr.: UNINA9910965151003321
• Pubbl/distr/stampa: Columbia, : University of Missouri Press, c2005
• ISBN: 0-8262-6478-6
• Edizione: [1st ed.]
• Descrizione fisica: xiv, 354 p
• Altri autori (Persone): EmbryCharles R. <1942->; CooperBarry <1943->; SandozEllis <1931-2023.>
• Disciplina: 190
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Bibliographic Level Mode of Issuance: Monograph
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: The turn toward existence as existence in the turn / David Walsh -- Hunting and political philosophy : an interpretation of the Kynegetikos / Barry Cooper -- The (anti-)eschatological perspective in Sigmund Freud's psychoanalysis / Gilbert Weiss -- Eric Voegelin's defense of human dignity / Glenn Hughes -- Eric Voegelin and a new science of politics / James L. Wiser -- The big mystery : human emergence as cosmic metaxy / Brendan Purcell -- A discipline of the mind and heart : Voegelin and Santayana as philosophers of experience / Elizabeth Corey -- Compactness, poetic ambiguity, and the fiction of Robert Penn Warren / Steve Ealy -- Biographies of consciousness : Peter Nadas and Eric Voegelin / Charles R. Embry -- Imagining modern Japan : Natsume Soseki's first trilogy / Timothy Hoye -- The concept of "the political" revisited / Jurgen Gebhardt -- Eric Voegelin on the nature of law / Timothy Fuller -- A classical prince : the style of Francois Mitterrand / Tilo Schabert -- Common sense and the rule of law : returning Voegelin to Central Europe / Martin Palous -- Civilizational conflict and spiritual disorder / Michael Franz -- Voegelin's puritan gnosticism and Bacon's great instauration / Stephen McKnight -- History as open horizon : Eric Voegelin's search for a post-imperial ; order / Thomas Hollweck.
• Sommario/riassunto: "Festschrift honoring Ellis Sandoz, director of the Eric Voegelin Institute for American Renaissance Studies and editor of Collected Works of Eric Voegelin. Essays explore philosophy, literature, and politics, and focus on Xenophon, Natsume, Freud, Robert Penn Warren, and George Santayana"--Provided by publisher.