Multi Objective Machine Learning
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Multi Objective Machine Learning
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Author : Yaochu Jin
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-02-10
Multi Objective Machine Learning written by Yaochu Jin and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-02-10 with Computers categories.
Recently, increasing interest has been shown in applying the concept of Pareto-optimality to machine learning, particularly inspired by the successful developments in evolutionary multi-objective optimization. It has been shown that the multi-objective approach to machine learning is particularly successful to improve the performance of the traditional single objective machine learning methods, to generate highly diverse multiple Pareto-optimal models for constructing ensembles models and, and to achieve a desired trade-off between accuracy and interpretability of neural networks or fuzzy systems. This monograph presents a selected collection of research work on multi-objective approach to machine learning, including multi-objective feature selection, multi-objective model selection in training multi-layer perceptrons, radial-basis-function networks, support vector machines, decision trees, and intelligent systems.
Multi Objective Machine Learning
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Author : Yaochu Jin
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-06-10
Multi Objective Machine Learning written by Yaochu Jin and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-06-10 with Technology & Engineering categories.
Recently, increasing interest has been shown in applying the concept of Pareto-optimality to machine learning, particularly inspired by the successful developments in evolutionary multi-objective optimization. It has been shown that the multi-objective approach to machine learning is particularly successful to improve the performance of the traditional single objective machine learning methods, to generate highly diverse multiple Pareto-optimal models for constructing ensembles models and, and to achieve a desired trade-off between accuracy and interpretability of neural networks or fuzzy systems. This monograph presents a selected collection of research work on multi-objective approach to machine learning, including multi-objective feature selection, multi-objective model selection in training multi-layer perceptrons, radial-basis-function networks, support vector machines, decision trees, and intelligent systems.
Multi Objective Decision Making
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Author : Diederik M. Roijers
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2017-04-20
Multi Objective Decision Making written by Diederik M. Roijers and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-04-20 with Computers categories.
Many real-world decision problems have multiple objectives. For example, when choosing a medical treatment plan, we want to maximize the efficacy of the treatment, but also minimize the side effects. These objectives typically conflict, e.g., we can often increase the efficacy of the treatment, but at the cost of more severe side effects. In this book, we outline how to deal with multiple objectives in decision-theoretic planning and reinforcement learning algorithms. To illustrate this, we employ the popular problem classes of multi-objective Markov decision processes (MOMDPs) and multi-objective coordination graphs (MO-CoGs). First, we discuss different use cases for multi-objective decision making, and why they often necessitate explicitly multi-objective algorithms. We advocate a utility-based approach to multi-objective decision making, i.e., that what constitutes an optimal solution to a multi-objective decision problem should be derived from the available information about user utility. We show how different assumptions about user utility and what types of policies are allowed lead to different solution concepts, which we outline in a taxonomy of multi-objective decision problems. Second, we show how to create new methods for multi-objective decision making using existing single-objective methods as a basis. Focusing on planning, we describe two ways to creating multi-objective algorithms: in the inner loop approach, the inner workings of a single-objective method are adapted to work with multi-objective solution concepts; in the outer loop approach, a wrapper is created around a single-objective method that solves the multi-objective problem as a series of single-objective problems. After discussing the creation of such methods for the planning setting, we discuss how these approaches apply to the learning setting. Next, we discuss three promising application domains for multi-objective decision making algorithms: energy, health, and infrastructure and transportation. Finally, we conclude by outlining important open problems and promising future directions.
Swarm Intelligence For Multi Objective Problems In Data Mining
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Author : Carlos Coello Coello
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-09-28
Swarm Intelligence For Multi Objective Problems In Data Mining written by Carlos Coello Coello and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-09-28 with Mathematics categories.
The purpose of this book is to collect contributions that are at the intersection of multi-objective optimization, swarm intelligence (specifically, particle swarm optimization and ant colony optimization) and data mining.
Multi Objective Memetic Algorithms
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Author : Chi-Keong Goh
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-02-26
Multi Objective Memetic Algorithms written by Chi-Keong Goh and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-02-26 with Mathematics categories.
The application of sophisticated evolutionary computing approaches for solving complex problems with multiple conflicting objectives in science and engineering have increased steadily in the recent years. Within this growing trend, Memetic algorithms are, perhaps, one of the most successful stories, having demonstrated better efficacy in dealing with multi-objective problems as compared to its conventional counterparts. Nonetheless, researchers are only beginning to realize the vast potential of multi-objective Memetic algorithm and there remain many open topics in its design. This book presents a very first comprehensive collection of works, written by leading researchers in the field, and reflects the current state-of-the-art in the theory and practice of multi-objective Memetic algorithms. "Multi-Objective Memetic algorithms" is organized for a wide readership and will be a valuable reference for engineers, researchers, senior undergraduates and graduate students who are interested in the areas of Memetic algorithms and multi-objective optimization.
Artificial Neural Networks And Machine Learning Icann 2023
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Author : Lazaros Iliadis
language : en
Publisher: Springer Nature
Release Date : 2023-09-21
Artificial Neural Networks And Machine Learning Icann 2023 written by Lazaros Iliadis and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-21 with Computers categories.
The 10-volume set LNCS 14254-14263 constitutes the proceedings of the 32nd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2023, which took place in Heraklion, Crete, Greece, during September 26–29, 2023. The 426 full papers, 9 short papers and 9 abstract papers included in these proceedings were carefully reviewed and selected from 947 submissions. ICANN is a dual-track conference, featuring tracks in brain inspired computing on the one hand, and machine learning on the other, with strong cross-disciplinary interactions and applications.
Multi Objective Swarm Intelligent Systems
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Author : Leandro dos Santos Coelho
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-11-19
Multi Objective Swarm Intelligent Systems written by Leandro dos Santos Coelho and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-11-19 with Computers categories.
This book covers the latest in multi-objective swarm intelligence and cooperative behavior. It contains innovative and intriguing applications as well as additions to the methodology and theory of genetic programming.
Reinforcement Learning For Cyber Operations
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Author : Abdul Rahman
language : en
Publisher: John Wiley & Sons
Release Date : 2024-12-27
Reinforcement Learning For Cyber Operations written by Abdul Rahman and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-27 with Computers categories.
A comprehensive and up-to-date application of reinforcement learning concepts to offensive and defensive cybersecurity In Reinforcement Learning for Cyber Operations: Applications of Artificial Intelligence for Penetration Testing, a team of distinguished researchers delivers an incisive and practical discussion of reinforcement learning (RL) in cybersecurity that combines intelligence preparation for battle (IPB) concepts with multi-agent techniques. The authors explain how to conduct path analyses within networks, how to use sensor placement to increase the visibility of adversarial tactics and increase cyber defender efficacy, and how to improve your organization’s cyber posture with RL and illuminate the most probable adversarial attack paths in your networks. Containing entirely original research, this book outlines findings and real-world scenarios that have been modeled and tested against custom generated networks, simulated networks, and data. You’ll also find: A thorough introduction to modeling actions within post-exploitation cybersecurity events, including Markov Decision Processes employing warm-up phases and penalty scaling Comprehensive explorations of penetration testing automation, including how RL is trained and tested over a standard attack graph construct Practical discussions of both red and blue team objectives in their efforts to exploit and defend networks, respectively Complete treatment of how reinforcement learning can be applied to real-world cybersecurity operational scenarios Perfect for practitioners working in cybersecurity, including cyber defenders and planners, network administrators, and information security professionals, Reinforcement Learning for Cyber Operations: Applications of Artificial Intelligence for Penetration Testing will also benefit computer science researchers.
Multi Objective Decision Making
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Author : Diederik M. Roijers
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Multi Objective Decision Making written by Diederik M. Roijers and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.
Many real-world decision problems have multiple objectives. For example, when choosing a medical treatment plan, we want to maximize the efficacy of the treatment, but also minimize the side effects. These objectives typically conflict, e.g., we can often increase the efficacy of the treatment, but at the cost of more severe side effects. In this book, we outline how to deal with multiple objectives in decision-theoretic planning and reinforcement learning algorithms. To illustrate this, we employ the popular problem classes of multi-objective Markov decision processes (MOMDPs) and multi-objective coordination graphs (MO-CoGs). First, we discuss different use cases for multi-objective decision making, and why they often necessitate explicitly multi-objective algorithms. We advocate a utility-based approach to multi-objective decision making, i.e., that what constitutes an optimal solution to a multi-objective decision problem should be derived from the available information about user utility. We show how different assumptions about user utility and what types of policies are allowed lead to different solution concepts, which we outline in a taxonomy of multi-objective decision problems. Second, we show how to create new methods for multi-objective decision making using existing single-objective methods as a basis. Focusing on planning, we describe two ways to creating multi-objective algorithms: in the inner loop approach, the inner workings of a single-objective method are adapted to work with multi-objective solution concepts; in the outer loop approach, a wrapper is created around a single-objective method that solves the multi-objective problem as a series of single-objective problems. After discussing the creation of such methods for the planning setting, we discuss how these approaches apply to the learning setting. Next, we discuss three promising application domains for multi-objective decision making algorithms: energy, health, and infrastructure and transportation. Finally, we conclude by outlining important open problems and promising future directions.
Evolutionary Large Scale Multi Objective Optimization And Applications
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Author : Xingyi Zhang
language : en
Publisher: John Wiley & Sons
Release Date : 2024-07-22
Evolutionary Large Scale Multi Objective Optimization And Applications written by Xingyi Zhang and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-22 with Technology & Engineering categories.
Tackle the most challenging problems in science and engineering with these cutting-edge algorithms Multi-objective optimization problems (MOPs) are those in which more than one objective needs to be optimized simultaneously. As a ubiquitous component of research and engineering projects, these problems are notoriously challenging. In recent years, evolutionary algorithms (EAs) have shown significant promise in their ability to solve MOPs, but challenges remain at the level of large-scale multi-objective optimization problems (LSMOPs), where the number of variables increases and the optimized solution is correspondingly harder to reach. Evolutionary Large-Scale Multi-Objective Optimization and Applications constitutes a systematic overview of EAs and their capacity to tackle LSMOPs. It offers an introduction to both the problem class and the algorithms before delving into some of the cutting-edge algorithms which have been specifically adapted to solving LSMOPs. Deeply engaged with specific applications and alert to the latest developments in the field, it’s a must-read for students and researchers facing these famously complex but crucial optimization problems. The book’s readers will also find: Analysis of multi-optimization problems in fields such as machine learning, network science, vehicle routing, and more Discussion of benchmark problems and performance indicators for LSMOPs Presentation of a new taxonomy of algorithms in the field Evolutionary Large-Scale Multi-Objective Optimization and Applications is ideal for advanced students, researchers, and scientists and engineers facing complex optimization problems.