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检索条件"丛书名=Synthesis Lectures on Artificial Intelligence and Machine Learning"

共 95 条记录，以下是81-90 订阅

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Essential Principles for Autonomous Robotics 1

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丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Henry Hexmoor

ISBN: (数字)9783031015632

ISBN: (纸本)9783031004353

From driving, flying, and swimming, to digging for unknown objects in space exploration, autonomous robots take on varied shapes and sizes. In part, autonomous robots are designed to perform tasks that are too dirty, dull, or dangerous for humans. With nontrivial autonomy and volition, they may soon claim their own place in human society. These robots will be our allies as we strive for understanding our natural and man-made environments and build positive synergies around us. Although we may never perfect replication of biological capabilities in robots, we must harness the inevitable emergence of robots that synchronizes with our own capacities to live, learn, and grow. This book is a snapshot of motivations and methodologies for our collective attempts to transform our lives and enable us to cohabit with robots that work with and for us. It reviews and guides the reader to seminal and continual developments that are the foundations for successful paradigms. It attempts to demystifythe abilities and limitations of robots. It is a progress report on the continuing work that will fuel future endeavors. Table of Contents: Part I: Preliminaries/Agency, Motion, and Anatomy/Behaviors / Architectures / Affect/Sensors / Manipulators/Part II: Mobility/Potential Fields/Roadmaps / Reactive Navigation / Multi-Robot Mapping: Brick and Mortar Strategy / Part III: State of the Art / Multi-Robotics Phenomena / Human-Robot Interaction / Fuzzy Control / Decision Theory and Game Theory / Part IV: On the Horizon / Applications: Macro and Micro Robots / References / Author Biography / Discussion

关键词： artificial intelligence machine learning Mathematical Models of Cognitive Processes and Neural Networks

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Active learning 1

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丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Burr Settles

ISBN: (数字)9783031015601

ISBN: (纸本)9783031004322

The key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed to choose the data from which it learns. An active learner may pose "queries," usually in the form of unlabeled data instances to be labeled by an "oracle" (e.g., a human annotator) that already understands the nature of the problem. This sort of approach is well-motivated in many modern machine learning and data mining applications, where unlabeled data may be abundant or easy to come by, but training labels are difficult, time-consuming, or expensive to obtain. This book is a general introduction to active learning. It outlines several scenarios in which queries might be formulated, and details many query selection algorithms which have been organized into four broad categories, or "query selection frameworks." We also touch on some of the theoretical foundations of active learning, and conclude with an overview of the strengths and weaknesses of these approaches in practice, including a summary of ongoing work to address these open challenges and opportunities. Table of Contents: Automating Inquiry / Uncertainty Sampling / Searching Through the Hypothesis Space / Minimizing Expected Error and Variance / Exploiting Structure in Data / Theory / Practical Considerations

关键词： artificial intelligence machine learning Mathematical Models of Cognitive Processes and Neural Networks

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Toward Robots That Reason: Logic, Probability & Causal Laws 1

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丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Vaishak Belle

ISBN: (数字)9783031210037

ISBN: (纸本)9783031210020;9783031210051

This book discusses the two fundamental elements that underline the science and design of artificial intelligence (AI) systems: the learning and acquisition of knowledge from observational data, and the reasoning of that knowledge together with whatever information is available about the application at hand. It then presents a mathematical treatment of the core issues that arise when unifying first-order logic and probability, especially in the presence of dynamics, including physical actions, sensing actions and their effects. A model for expressing causal laws describing dynamics is also considered, along with computational ideas for reasoning with such laws over probabilistic logical knowledge.

关键词： artificial intelligence Robotics Probability and Statistics in Computer Science Logic Design Computer Applications Data Mining and Knowledge Discovery

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Introduction to Graph Neural Networks 1

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丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Zhiyuan Liu Jie Zhou

ISBN: (数字)9783031015878

ISBN: (纸本)9783031004599

This book provides a comprehensive introduction to the basic concepts, models, and applications of graph neural networks. It starts with the introduction of the vanilla GNN model. Then several variants of the vanilla model are introduced such as graph convolutional networks, graph recurrent networks, graph attention networks, graph residual networks, and several general frameworks. Variants for different graph types and advanced training methods are also included. As for the applications of GNNs, the book categorizes them into structural, non-structural, and other scenarios, and then it introduces several typical models on solving these tasks. Finally, the closing chapters provide GNN open resources and the outlook of several future directions.

关键词： artificial intelligence machine learning Mathematical Models of Cognitive Processes and Neural Networks

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Judgment Aggregation: A Primer 1

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丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Davide Grossi Gabriella Pigozzi

ISBN: (数字)9783031015687

ISBN: (纸本)9783031004407

Judgment aggregation is a mathematical theory of collective decision-making. It concerns the methods whereby individual opinions about logically interconnected issues of interest can, or cannot, be aggregated into one collective stance. Aggregation problems have traditionally been of interest for disciplines like economics and the political sciences, as well as philosophy, where judgment aggregation itself originates from, but have recently captured the attention of disciplines like computer science, artificial intelligence and multi-agent systems. Judgment aggregation has emerged in the last decade as a unifying paradigm for the formalization and understanding of aggregation problems. Still, no comprehensive presentation of the theory is available to date. This synthesis Lecture aims at filling this gap presenting the key motivations, results, abstractions and techniques underpinning it. Table of Contents: Preface / Acknowledgments / Logic Meets Social Choice Theory / Basic Concepts /Impossibility / Coping with Impossibility / Manipulability / Aggregation Rules / Deliberation / Bibliography / Authors' Biographies / Index

关键词： artificial intelligence machine learning Mathematical Models of Cognitive Processes and Neural Networks

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Introduction to Semi-Supervised learning 1

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丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Xiaojin Zhu Andrew B. Goldberg

ISBN: (数字)9783031015489

ISBN: (纸本)9783031004209

Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data are labeled. The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and design algorithms that take advantage of such a combination. Semi-supervised learning is of great interest in machine learning and data mining because it can use readily available unlabeled data to improve supervised learning tasks when the labeled data are scarce or expensive. Semi-supervised learning also shows potential as a quantitative tool to understand human category learning, where most of the input is self-evidently unlabeled. In this introductory book, we present some popular semi-supervised learning models, including self-training, mixture models, co-training and multiview learning, graph-based methods, and semi-supervised support vector machines. For each model, we discuss its basic mathematical formulation. The success of semi-supervised learning depends critically on some underlying assumptions. We emphasize the assumptions made by each model and give counterexamples when appropriate to demonstrate the limitations of the different models. In addition, we discuss semi-supervised learning for cognitive psychology. Finally, we give a computational learning theoretic perspective on semi-supervised learning, and we conclude the book with a brief discussion of open questions in the field. Table of Contents: Introduction to Statistical machine learning / Overview of Semi-Supervised learning / Mixture Models and EM / Co-Training / Graph-Based Semi-Supervised learning / Semi-Supervised Support Vector machines/

关键词： artificial intelligence machine learning Mathematical Models of Cognitive Processes and Neural Networks

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Federated learning 1

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丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Qiang Yang Yang Liu Yong Cheng Yan Kang Tianjian Chen Han Yu

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Metric learning 1

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丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Aurélien Bellet Amaury Habrard Marc Sebban

ISBN: (数字)9783031015724

ISBN: (纸本)9783031004445

Similarity between objects plays an important role in both human cognitive processes and artificial systems for recognition and categorization. How to appropriately measure such similarities for a given task is crucial to the performance of many machine learning, pattern recognition and data mining methods. This book is devoted to metric learning, a set of techniques to automatically learn similarity and distance functions from data that has attracted a lot of interest in machine learning and related fields in the past ten years. In this book, we provide a thorough review of the metric learning literature that covers algorithms, theory and applications for both numerical and structured data. We first introduce relevant definitions and classic metric functions, as well as examples of their use in machine learning and data mining. We then review a wide range of metric learning algorithms, starting with the simple setting of linear distance and similarity learning. We show how one may scale-up these methods to very large amounts of training data. To go beyond the linear case, we discuss methods that learn nonlinear metrics or multiple linear metrics throughout the feature space, and review methods for more complex settings such as multi-task and semi-supervised learning. Although most of the existing work has focused on numerical data, we cover the literature on metric learning for structured data like strings, trees, graphs and time series. In the more technical part of the book, we present some recent statistical frameworks for analyzing the generalization performance in metric learning and derive results for some of the algorithms presented earlier. Finally, we illustrate the relevance of metric learning in real-world problems through a series of successful applications to computer vision, bioinformatics and information retrieval. Table of Contents: Introduction / Metrics / Properties of Metric learning Algorithms / Linear Metric learning / Nonlinear and Local Metri

关键词： artificial intelligence machine learning Mathematical Models of Cognitive Processes and Neural Networks

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An Introduction to Constraint-Based Temporal Reasoning 1

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丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Roman Barták Robert A. Morris K. Brent Venable

ISBN: (数字)9783031015670

ISBN: (纸本)9783031004391

Solving challenging computational problems involving time has been a critical component in the development of artificial intelligence systems almost since the inception of the field. This book provides a concise introduction to the core computational elements of temporal reasoning for use in AI systems for planning and scheduling, as well as systems that extract temporal information from data. It presents a survey of temporal frameworks based on constraints, both qualitative and quantitative, as well as of major temporal consistency techniques. The book also introduces the reader to more recent extensions to the core model that allow AI systems to explicitly represent temporal preferences and temporal uncertainty. This book is intended for students and researchers interested in constraint-based temporal reasoning. It provides a self-contained guide to the different representations of time, as well as examples of recent applications of time in AI systems.

关键词： artificial intelligence machine learning Mathematical Models of Cognitive Processes and Neural Networks

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learning with Support Vector machines 1

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丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Colin Campbell Yiming Ying

ISBN: (数字)9783031015526

ISBN: (纸本)9783031004247

Support Vectors machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees. Finally, we give an overview of the main types of kernels which are used in practice and how to learn and make predictions from multiple types of input data. Table of Contents: Support Vector machines for Classification / Kernel-based Models / learning with Kernels

关键词： artificial intelligence machine learning Mathematical Models of Cognitive Processes and Neural Networks

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