Convexity and connectivity of the solution space in machine learning problems PDF Download
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Author: Maxime Hardy
Publisher: Scientia Rerum (academic publishers), Paris
ISBN:
Category : Mathematics
Languages : en
Pages : 215
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Book Description
ScientiaRerum Thesis — 2018. This thesis investigates properties of the solution space of the machine-learning problem of random pattern classification. Such properties as convexity of the space of solutions, its connectivity and clusterization are studied. Evidence has been provided recently that there exists a universality class for random pattern classification models, making it possible to study the properties of the whole set of constraint satisfaction problems using the most simple model, the perceptron with spherical constraint: it is exactly solvable and exhibits the full stack of charactetistic properties of that class. In order to obtain statistically representative treatment of the model (as opposed to the best/worst-case scenarios), we used the well established methods of theoretical physics of disordered systems (a.k.a. spin glasses). In terms of that science, this model can be interpreted as a random packing problem and demonstrates the phenomenology of slow glassy relaxation and a jamming transition. The specific property of that model is that the corresponding constraint satisfaction problems ceases to be convex. The non-convex domain is exproled in detail in this thesis and its structure is presented on a phase diagram.Publisher : Scientia Rerum (academic publishers), Paris
Author: Maxime Hardy
Publisher: Scientia Rerum (academic publishers), Paris
ISBN:
Category : Mathematics
Languages : en
Pages : 215
Get Book Here
Book Description
ScientiaRerum Thesis — 2018. This thesis investigates properties of the solution space of the machine-learning problem of random pattern classification. Such properties as convexity of the space of solutions, its connectivity and clusterization are studied. Evidence has been provided recently that there exists a universality class for random pattern classification models, making it possible to study the properties of the whole set of constraint satisfaction problems using the most simple model, the perceptron with spherical constraint: it is exactly solvable and exhibits the full stack of charactetistic properties of that class. In order to obtain statistically representative treatment of the model (as opposed to the best/worst-case scenarios), we used the well established methods of theoretical physics of disordered systems (a.k.a. spin glasses). In terms of that science, this model can be interpreted as a random packing problem and demonstrates the phenomenology of slow glassy relaxation and a jamming transition. The specific property of that model is that the corresponding constraint satisfaction problems ceases to be convex. The non-convex domain is exproled in detail in this thesis and its structure is presented on a phase diagram.Publisher : Scientia Rerum (academic publishers), Paris
Author: Niall Griffith
Publisher: MIT Press
ISBN: 9780262071819
Category : Music
Languages : en
Pages : 422
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Book Description
This volume presents the most up-to-date collection of neural network models of music and creativity gathered together in one place. Chapters by leaders in the field cover new connectionist models of pitch perception, tonality, musical streaming, sequential and hierarchical melodic structure, composition, harmonization, rhythmic analysis, sound generation, and creative evolution. The collection combines journal papers on connectionist modeling, cognitive science, and music perception with new papers solicited for this volume. It also contains an extensive bibliography of related work. Contributors Shumeet Baluja, M.I. Bellgard, Michael A. Casey, Garrison W. Cottrell, Peter Desain, Robert O. Gjerdingen, Mike Greenhough, Niall Griffith, Stephen Grossberg, Henkjan Honing, Todd Jochem, Bruce F. Katz, John F. Kolen, Edward W. Large, Michael C. Mozer, Michael P.A. Page, Caroline Palmer, Jordan B. Pollack, Dean Pomerleau, Stephen W. Smoliar, Ian Taylor, Peter M. Todd, C.P. Tsang, Gregory M. Werner
Author: Musaddiq Al Ali
Publisher: Springer Nature
ISBN: 9819710405
Category :
Languages : en
Pages : 143
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Book Description
Author: Haibin Yu
Publisher: Springer Nature
ISBN: 9819903890
Category : Technology & Engineering
Languages : en
Pages : 178
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Book Description
With the rapid proliferation of information and communications technology, industrial automation has undergone a sweeping transformation toward intelligent manufacturing. Wireless communication is widely considered to be one of the key technologies enabling intelligent manufacturing. On one hand, deterministic communication with high reliability and low latency is typically required in industrial automation applications. On the other hand, wireless communication in industrial settings is hindered by strictly limited communication resources and many other factors which mainly derive from the shared and error-prone nature of the wireless channels used. The limited communication resources and harsh channel conditions pose considerable challenges for reliable, real-time data transmission in industrial wireless networks. Resource optimization methods are vital to ensuring the deterministic performance of industrial wireless networks. Traditional resource optimization methods adopt the isolated resource optimization methods for each protocol layer, which is inherently local-optimal and leads performance uncontrollable. To focus on “Performance Controllable Industrial Wireless Networks”, this book presents thejoint resource optimization methods across multiple protocol layers for industrial wireless networks; reviews recent, major advances; and discusses the practical implementations of the proposed methods. The joint resource optimization methods discussed here will greatly benefit scientists and researchers in the areas of industrial automation and Industrial Internet of Things. To gain the most from this book, readers should have a fundamental grasp of wireless communication, scheduling theory, and convex optimization.
Author: Shai Shalev-Shwartz
Publisher: Cambridge University Press
ISBN: 1107057132
Category : Computers
Languages : en
Pages : 415
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Book Description
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Author: Suman Lata Tripathi
Publisher: John Wiley & Sons
ISBN: 1119861829
Category : Technology & Engineering
Languages : en
Pages : 516
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Book Description
Enables readers to understand the fundamental concepts of machine and deep learning techniques with interactive, real-life applications within signal and image processing Machine Learning Algorithms for Signal and Image Processing aids the reader in designing and developing real-world applications using advances in machine learning to aid and enhance speech signal processing, image processing, computer vision, biomedical signal processing, adaptive filtering, and text processing. It includes signal processing techniques applied for pre-processing, feature extraction, source separation, or data decompositions to achieve machine learning tasks. Written by well-qualified authors and contributed to by a team of experts within the field, the work covers a wide range of important topics, such as: Speech recognition, image reconstruction, object classification and detection, and text processing Healthcare monitoring, biomedical systems, and green energy How various machine and deep learning techniques can improve accuracy, precision rate recall rate, and processing time Real applications and examples, including smart sign language recognition, fake news detection in social media, structural damage prediction, and epileptic seizure detection Professionals within the field of signal and image processing seeking to adapt their work further will find immense value in this easy-to-understand yet extremely comprehensive reference work. It is also a worthy resource for students and researchers in related fields who are looking to thoroughly understand the historical and recent developments that have been made in the field.
Author: Michael C. Mozer
Publisher: Psychology Press
ISBN: 1317780523
Category : Psychology
Languages : en
Pages : 428
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Book Description
The result of the 1993 Connectionist Models Summer School, the papers in this volume exemplify the tremendous breadth and depth of research underway in the field of neural networks. Although the slant of the summer school has always leaned toward cognitive science and artificial intelligence, the diverse scientific backgrounds and research interests of accepted students and invited faculty reflect the broad spectrum of areas contributing to neural networks, including artificial intelligence, cognitive science, computer science, engineering, mathematics, neuroscience, and physics. Providing an accurate picture of the state of the art in this fast-moving field, the proceedings of this intense two-week program of lectures, workshops, and informal discussions contains timely and high-quality work by the best and the brightest in the neural networks field.
Author: Suvrit Sra
Publisher: MIT Press
ISBN: 026201646X
Category : Computers
Languages : en
Pages : 509
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Book Description
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.
Author: Michael Wand
Publisher: Springer Nature
ISBN: 303172335X
Category :
Languages : en
Pages : 486
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Book Description
Author: Mehryar Mohri
Publisher: MIT Press
ISBN: 0262351366
Category : Computers
Languages : en
Pages : 505
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Book Description
A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.
