Lifelong Machine Learning Second Edition

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Lifelong Machine Learning

Zhiyuan Chen,Bing Liu
Lifelong Machine Learning Book PDF

Author : Zhiyuan Chen,Bing Liu
Publisher : Morgan & Claypool Publishers
Page : 209 pages
File Size : 49,6 Mb
Release : 2018-08-14
Category : Computers
ISBN : 9781681733036

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Lifelong Machine Learning by Zhiyuan Chen,Bing Liu Pdf

Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent. Research in lifelong learning has developed significantly in the relatively short time since the first edition of this book was published. The purpose of this second edition is to expand the definition of lifelong learning, update the content of several chapters, and add a new chapter about continual learning in deep neural networks—which has been actively researched over the past two or three years. A few chapters have also been reorganized to make each of them more coherent for the reader. Moreover, the authors want to propose a unified framework for the research area. Currently, there are several research topics in machine learning that are closely related to lifelong learning—most notably, multi-task learning, transfer learning, and meta-learning—because they also employ the idea of knowledge sharing and transfer. This book brings all these topics under one roof and discusses their similarities and differences. Its goal is to introduce this emerging machine learning paradigm and present a comprehensive survey and review of the important research results and latest ideas in the area. This book is thus suitable for students, researchers, and practitioners who are interested in machine learning, data mining, natural language processing, or pattern recognition. Lecturers can readily use the book for courses in any of these related fields.

Lifelong Machine Learning

Zhiyuan Chen (Computer scientist)
Lifelong Machine Learning Book PDF

Author : Zhiyuan Chen (Computer scientist)
Publisher : Unknown
Page : 0 pages
File Size : 40,5 Mb
Release : 2024-06-07
Category : Machine learning
ISBN : 3031027094

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Lifelong Machine Learning by Zhiyuan Chen (Computer scientist) Pdf

This is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent. Research in lifelong learning has developed significantly in the relatively short time since the first edition of this book was published. The purpose of this second edition is to expand the definition of lifelong learning, update the content of several chapters, and add a new chapter about continual learning in deep neural networks--which has been actively researched over the past two or three years. A few chapters have also been reorganized to make each of them more coherent for the reader. Moreover, the authors want to propose a unified framework for the research area. Currently, there are several research topics in machine learning that are closely related to lifelong learning--most notably, multi-task learning, transfer learning, and metalearning--because they also employ the idea of knowledge sharing and transfer. This book brings all these topics under one roof and discusses their similarities and differences. Its goal is to introduce this emerging machine learning paradigm and present a comprehensive survey and review of the important research results and latest ideas in the area. This book is thus suitable for students, researchers, and practitioners who are interested in machine learning, data mining, natural language processing, or pattern recognition. Lecturers can readily use the book for courses in any of these related fields.

Lifelong Machine Learning

Chen Zhiyuan,Bing Liu
Lifelong Machine Learning Book PDF

Author : Chen Zhiyuan,Bing Liu
Publisher : Unknown
Page : 187 pages
File Size : 40,9 Mb
Release : 2018
Category : Machine learning
ISBN : 1681733994

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Lifelong Machine Learning by Chen Zhiyuan,Bing Liu Pdf

"This is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent. Research in lifelong learning has developed significantly in the relatively short time since the first edition of this book was published. The purpose of this second edition is to expand the definition of lifelong learning, update the content of several chapters, and add a new chapter about continual learning in deep neural networks--which has been actively researched over the past two or three years. A few chapters have also been reorganized to make each of them more coherent for the reader. Moreover, the authors want to propose a unified framework for the research area. Currently, there are several research topics in machine learning that are closely related to lifelong learning--most notably, multi-task learning, transfer learning, and metalearning--because they also employ the idea of knowledge sharing and transfer. This book brings all these topics under one roof and discusses their similarities and differences. Its goal is to introduce this emerging machine learning paradigm and present a comprehensive survey and review of the important research results and latest ideas in the area. This book is thus suitable for students, researchers, and practitioners who are interested in machine learning, data mining, natural language processing, or pattern recognition. Lecturers can readily use the book for courses in any of these related fields."--Provided by publisher.

Lifelong Machine Learning, Second Edition

Zhiyuan Sun,Bing Leno da Silva
Lifelong Machine Learning Second Edition Book PDF

Author : Zhiyuan Sun,Bing Leno da Silva
Publisher : Springer Nature
Page : 187 pages
File Size : 43,6 Mb
Release : 2022-06-01
Category : Computers
ISBN : 9783031015816

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Lifelong Machine Learning, Second Edition by Zhiyuan Sun,Bing Leno da Silva Pdf

Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent. Research in lifelong learning has developed significantly in the relatively short time since the first edition of this book was published. The purpose of this second edition is to expand the definition of lifelong learning, update the content of several chapters, and add a new chapter about continual learning in deep neural networks—which has been actively researched over the past two or three years. A few chapters have also been reorganized to make each of them more coherent for the reader. Moreover, the authors want to propose a unified framework for the research area. Currently, there are several research topics in machine learning that are closely related to lifelong learning—most notably, multi-task learning, transfer learning, and meta-learning—because they also employ the idea of knowledge sharing and transfer. This book brings all these topics under one roof and discusses their similarities and differences. Its goal is to introduce this emerging machine learning paradigm and present a comprehensive survey and review of the important research results and latest ideas in the area. This book is thus suitable for students, researchers, and practitioners who are interested in machine learning, data mining, natural language processing, or pattern recognition. Lecturers can readily use the book for courses in any of these related fields.

Data Mining

Ian H. Witten,Eibe Frank,Mark A. Hall
Data Mining Book PDF

Author : Ian H. Witten,Eibe Frank,Mark A. Hall
Publisher : Elsevier
Page : 665 pages
File Size : 49,7 Mb
Release : 2011-02-03
Category : Computers
ISBN : 9780080890364

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Data Mining by Ian H. Witten,Eibe Frank,Mark A. Hall Pdf

Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise. Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

Increasing Naturalness and Flexibility in Spoken Dialogue Interaction

Erik Marchi,Sabato Marco Siniscalchi,Sandro Cumani,Valerio Mario Salerno,Haizhou Li
Increasing Naturalness and Flexibility in Spoken Dialogue Interaction Book PDF

Author : Erik Marchi,Sabato Marco Siniscalchi,Sandro Cumani,Valerio Mario Salerno,Haizhou Li
Publisher : Springer Nature
Page : 453 pages
File Size : 47,8 Mb
Release : 2021-03-10
Category : Technology & Engineering
ISBN : 9789811593239

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Increasing Naturalness and Flexibility in Spoken Dialogue Interaction by Erik Marchi,Sabato Marco Siniscalchi,Sandro Cumani,Valerio Mario Salerno,Haizhou Li Pdf

This book compiles and presents a synopsis on current global research efforts to push forward the state of the art in dialogue technologies, including advances to language and context understanding, and dialogue management, as well as human–robot interaction, conversational agents, question answering and lifelong learning for dialogue systems.

Transfer Learning for Multiagent Reinforcement Learning Systems

Felipe Felipe Leno da Silva,Anna Helena Reali Anna Helena Reali Costa
Transfer Learning for Multiagent Reinforcement Learning Systems Book PDF

Author : Felipe Felipe Leno da Silva,Anna Helena Reali Anna Helena Reali Costa
Publisher : Springer Nature
Page : 111 pages
File Size : 45,5 Mb
Release : 2022-06-01
Category : Computers
ISBN : 9783031015915

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Transfer Learning for Multiagent Reinforcement Learning Systems by Felipe Felipe Leno da Silva,Anna Helena Reali Anna Helena Reali Costa Pdf

Learning to solve sequential decision-making tasks is difficult. Humans take years exploring the environment essentially in a random way until they are able to reason, solve difficult tasks, and collaborate with other humans towards a common goal. Artificial Intelligent agents are like humans in this aspect. Reinforcement Learning (RL) is a well-known technique to train autonomous agents through interactions with the environment. Unfortunately, the learning process has a high sample complexity to infer an effective actuation policy, especially when multiple agents are simultaneously actuating in the environment. However, previous knowledge can be leveraged to accelerate learning and enable solving harder tasks. In the same way humans build skills and reuse them by relating different tasks, RL agents might reuse knowledge from previously solved tasks and from the exchange of knowledge with other agents in the environment. In fact, virtually all of the most challenging tasks currently solved by RL rely on embedded knowledge reuse techniques, such as Imitation Learning, Learning from Demonstration, and Curriculum Learning. This book surveys the literature on knowledge reuse in multiagent RL. The authors define a unifying taxonomy of state-of-the-art solutions for reusing knowledge, providing a comprehensive discussion of recent progress in the area. In this book, readers will find a comprehensive discussion of the many ways in which knowledge can be reused in multiagent sequential decision-making tasks, as well as in which scenarios each of the approaches is more efficient. The authors also provide their view of the current low-hanging fruit developments of the area, as well as the still-open big questions that could result in breakthrough developments. Finally, the book provides resources to researchers who intend to join this area or leverage those techniques, including a list of conferences, journals, and implementation tools. This book will be useful for a wide audience; and will hopefully promote new dialogues across communities and novel developments in the area.

Applying Reinforcement Learning on Real-World Data with Practical Examples in Python

Philip Osborne,Kajal Singh,Matthew E. Taylor
Applying Reinforcement Learning on Real World Data with Practical Examples in Python Book PDF

Author : Philip Osborne,Kajal Singh,Matthew E. Taylor
Publisher : Morgan & Claypool Publishers
Page : 109 pages
File Size : 46,6 Mb
Release : 2022-05-20
Category : Computers
ISBN : 9781636393452

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Applying Reinforcement Learning on Real-World Data with Practical Examples in Python by Philip Osborne,Kajal Singh,Matthew E. Taylor Pdf

Reinforcement learning is a powerful tool in artificial intelligence in which virtual or physical agents learn to optimize their decision making to achieve long-term goals. In some cases, this machine learning approach can save programmers time, outperform existing controllers, reach super-human performance, and continually adapt to changing conditions. It has shown human level performance on a number of tasks (REF) and the methodology for automation in robotics and self-driving cars (REF). This book argues that these successes show reinforcement learning can be adopted successfully in many different situations, including robot control, stock trading, supply chain optimization, and plant control. However, reinforcement learning has traditionally been limited to applications in virtual environments or simulations in which the setup is already provided. Furthermore, experimentation may be completed for an almost limitless number of attempts risk-free. In many real-life tasks, applying reinforcement learning is not as simple as (1) data is not in the correct form for reinforcement learning; (2) data is scarce, and (3) automation has limitations in the real-world. Therefore, this book is written to help academics, domain specialists, and data enthusiast alike to understand the basic principles of applying reinforcement learning to real-world problems. This is achieved by focusing on the process of taking practical examples and modeling standard data into the correct form required to then apply basic agents. To further assist readers gain a deep and grounded understanding of the approaches, the book shows hand-calculated examples in full and then how this can be achieved in a more automated manner with code. For decision makers who are interested in reinforcement learning as a solution but are not proficient, the book includes simple, non-technical examples in the introduction and case studies section. These provide context of what reinforcement learning offer but also the challenges and risks associated with applying it in practice. Specifically, these sections illustrate the differences between reinforcement learning and other machine learning approaches as well as how well-known companies have found success using the approach to their problems.

Lifelong Machine Learning

Zhiyuan Chaudhri,Bing Liu
Lifelong Machine Learning Book PDF

Author : Zhiyuan Chaudhri,Bing Liu
Publisher : Springer Nature
Page : 137 pages
File Size : 43,7 Mb
Release : 2016-11-07
Category : Computers
ISBN : 9783031015755

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Lifelong Machine Learning by Zhiyuan Chaudhri,Bing Liu Pdf

Lifelong Machine Learning (or Lifelong Learning) is an advanced machine learning paradigm that learns continuously, accumulates the knowledge learned in previous tasks, and uses it to help future learning. In the process, the learner becomes more and more knowledgeable and effective at learning. This learning ability is one of the hallmarks of human intelligence. However, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model. It makes no attempt to retain the learned knowledge and use it in future learning. Although this isolated learning paradigm has been very successful, it requires a large number of training examples, and is only suitable for well-defined and narrow tasks. In comparison, we humans can learn effectively with a few examples because we have accumulated so much knowledge in the past which enables us to learn with little data or effort. Lifelong learning aims to achieve this capability. As statistical machine learning matures, it is time to make a major effort to break the isolated learning tradition and to study lifelong learning to bring machine learning to new heights. Applications such as intelligent assistants, chatbots, and physical robots that interact with humans and systems in real-life environments are also calling for such lifelong learning capabilities. Without the ability to accumulate the learned knowledge and use it to learn more knowledge incrementally, a system will probably never be truly intelligent. This book serves as an introductory text and survey to lifelong learning.

Reasoning with Probabilistic and Deterministic Graphical Models

Rina Dechter
Reasoning with Probabilistic and Deterministic Graphical Models Book PDF

Author : Rina Dechter
Publisher : Morgan & Claypool Publishers
Page : 201 pages
File Size : 52,8 Mb
Release : 2019-02-14
Category : Computers
ISBN : 9781681734910

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Reasoning with Probabilistic and Deterministic Graphical Models by Rina Dechter Pdf

Graphical models (e.g., Bayesian and constraint networks, influence diagrams, and Markov decision processes) have become a central paradigm for knowledge representation and reasoning in both artificial intelligence and computer science in general. These models are used to perform many reasoning tasks, such as scheduling, planning and learning, diagnosis and prediction, design, hardware and software verification, and bioinformatics. These problems can be stated as the formal tasks of constraint satisfaction and satisfiability, combinatorial optimization, and probabilistic inference. It is well known that the tasks are computationally hard, but research during the past three decades has yielded a variety of principles and techniques that significantly advanced the state of the art. This book provides comprehensive coverage of the primary exact algorithms for reasoning with such models. The main feature exploited by the algorithms is the model's graph. We present inference-based, message-passing schemes (e.g., variable-elimination) and search-based, conditioning schemes (e.g., cycle-cutset conditioning and AND/OR search). Each class possesses distinguished characteristics and in particular has different time vs. space behavior. We emphasize the dependence of both schemes on few graph parameters such as the treewidth, cycle-cutset, and (the pseudo-tree) height. The new edition includes the notion of influence diagrams, which focus on sequential decision making under uncertainty. We believe the principles outlined in the book would serve well in moving forward to approximation and anytime-based schemes. The target audience of this book is researchers and students in the artificial intelligence and machine learning area, and beyond.

Reinforcement Learning, second edition

Richard S. Sutton,Andrew G. Barto
Reinforcement Learning second edition Book PDF

Author : Richard S. Sutton,Andrew G. Barto
Publisher : MIT Press
Page : 549 pages
File Size : 53,5 Mb
Release : 2018-11-13
Category : Computers
ISBN : 9780262039246

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Reinforcement Learning, second edition by Richard S. Sutton,Andrew G. Barto Pdf

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

Positive Unlabeled Learning

Hamed Mirzaei,Neda Rahimian,Hamid Reza Mirzaei,Javid Sadri Nahand,Michael R. Hamblin
Positive Unlabeled Learning Book PDF

Author : Hamed Mirzaei,Neda Rahimian,Hamid Reza Mirzaei,Javid Sadri Nahand,Michael R. Hamblin
Publisher : Springer Nature
Page : 134 pages
File Size : 51,9 Mb
Release : 2022-06-08
Category : Computers
ISBN : 9783031791789

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Positive Unlabeled Learning by Hamed Mirzaei,Neda Rahimian,Hamid Reza Mirzaei,Javid Sadri Nahand,Michael R. Hamblin Pdf

Machine learning and artificial intelligence (AI) are powerful tools that create predictive models, extract information, and help make complex decisions. They do this by examining an enormous quantity of labeled training data to find patterns too complex for human observation. However, in many real-world applications, well-labeled data can be difficult, expensive, or even impossible to obtain. In some cases, such as when identifying rare objects like new archeological sites or secret enemy military facilities in satellite images, acquiring labels could require months of trained human observers at incredible expense. Other times, as when attempting to predict disease infection during a pandemic such as COVID-19, reliable true labels may be nearly impossible to obtain early on due to lack of testing equipment or other factors. In that scenario, identifying even a small amount of truly negative data may be impossible due to the high false negative rate of available tests. In such problems, it is possible to label a small subset of data as belonging to the class of interest though it is impractical to manually label all data not of interest. We are left with a small set of positive labeled data and a large set of unknown and unlabeled data. Readers will explore this Positive and Unlabeled learning (PU learning) problem in depth. The book rigorously defines the PU learning problem, discusses several common assumptions that are frequently made about the problem and their implications, and considers how to evaluate solutions for this problem before describing several of the most popular algorithms to solve this problem. It explores several uses for PU learning including applications in biological/medical, business, security, and signal processing. This book also provides high-level summaries of several related learning problems such as one-class classification, anomaly detection, and noisy learning and their relation to PU learning.

Artificial Intelligence

RICHARD E.. JIANG NEAPOLITAN (XIA.),Xia Jiang
Artificial Intelligence Book PDF

Author : RICHARD E.. JIANG NEAPOLITAN (XIA.),Xia Jiang
Publisher : CRC Press
Page : 480 pages
File Size : 47,9 Mb
Release : 2020-06-30
Category : Electronic
ISBN : 0367571641

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Artificial Intelligence by RICHARD E.. JIANG NEAPOLITAN (XIA.),Xia Jiang Pdf

The first edition of this popular textbook, Contemporary Artificial Intelligence, provided an accessible and student friendly introduction to AI. This fully revised and expanded update retains the same accessibility and problem-solving approach, while providing new material and methods, including neural networks and deep learning.

Machine Learning Algorithms for Supervised and Unsupervised Learning

William Sullivan
Machine Learning Algorithms for Supervised and Unsupervised Learning Book PDF

Author : William Sullivan
Publisher : Createspace Independent Publishing Platform
Page : 156 pages
File Size : 52,7 Mb
Release : 2018-03-06
Category : Electronic
ISBN : 198624184X

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Machine Learning Algorithms for Supervised and Unsupervised Learning by William Sullivan Pdf

Machine Learning Second Edition I listened carefully to feedback from customers for my original book, and revamped this new edition. I'm excited to present you the second edition with various high quality diagrams, explanations, extensive information and so much more value packed within. What You'll Learn... Supervised Learning Unsupervised Learning Reinforced Learning Algorithms Decision Tree Random Forest And much, much more! Don't miss out on this opportunity to expand your knowledge base with the second edition of my original machine learning book in the artificial intelligence series. Other books easily retail for $50-$100+ and have far less quality content. This book is by far superior and exceeds any other book available. Make the greatest investment in yourself by investing in your knowledge! Buy Now *Note: For the best visual experience of diagrams it is highly recommend you purchase the paperback version*

Network Embedding

Cheng Cheng Yang,Zhiyuan Zhiyuan Liu,Cunchao Cunchao Tu,Chuan Chuan Shi,Maosong Maosong Sun
Network Embedding Book PDF

Author : Cheng Cheng Yang,Zhiyuan Zhiyuan Liu,Cunchao Cunchao Tu,Chuan Chuan Shi,Maosong Maosong Sun
Publisher : Springer Nature
Page : 220 pages
File Size : 51,9 Mb
Release : 2022-05-31
Category : Computers
ISBN : 9783031015908

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Network Embedding by Cheng Cheng Yang,Zhiyuan Zhiyuan Liu,Cunchao Cunchao Tu,Chuan Chuan Shi,Maosong Maosong Sun Pdf

heterogeneous graphs. Further, the book introduces different applications of NE such as recommendation and information diffusion prediction. Finally, the book concludes the methods and applications and looks forward to the future directions.