Machine Learning Methods for Planning

Author: Steven Minton

Publisher: Morgan Kaufmann

ISBN: 1483221172

Category: Social Science

Page: 554

View: 8421

Machine Learning Methods for Planning provides information pertinent to learning methods for planning and scheduling. This book covers a wide variety of learning methods and learning architectures, including analogical, case-based, decision-tree, explanation-based, and reinforcement learning. Organized into 15 chapters, this book begins with an overview of planning and scheduling and describes some representative learning systems that have been developed for these tasks. This text then describes a learning apprentice for calendar management. Other chapters consider the problem of temporal credit assignment and describe tractable classes of problems for which optimal plans can be derived. This book discusses as well how reactive, integrated systems give rise to new requirements and opportunities for machine learning. The final chapter deals with a method for learning problem decompositions, which is based on an idealized model of efficiency for problem-reduction search. This book is a valuable resource for production managers, planners, scientists, and research workers.

Artificial Neural Networks and Machine Learning – ICANN 2018

27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings

Author: Věra Kůrková,Yannis Manolopoulos,Barbara Hammer,Lazaros Iliadis,Ilias Maglogiannis

Publisher: Springer

ISBN: 3030014185

Category: Computers

Page: 824

View: 4847

This three-volume set LNCS 11139-11141 constitutes the refereed proceedings of the 27th International Conference on Artificial Neural Networks, ICANN 2018, held in Rhodes, Greece, in October 2018. The papers presented in these volumes was carefully reviewed and selected from total of 360 submissions. They are related to the following thematic topics: AI and Bioinformatics, Bayesian and Echo State Networks, Brain Inspired Computing, Chaotic Complex Models, Clustering, Mining, Exploratory Analysis, Coding Architectures, Complex Firing Patterns, Convolutional Neural Networks, Deep Learning (DL), DL in Real Time Systems, DL and Big Data Analytics, DL and Big Data, DL and Forensics, DL and Cybersecurity, DL and Social Networks, Evolving Systems – Optimization, Extreme Learning Machines, From Neurons to Neuromorphism, From Sensation to Perception, From Single Neurons to Networks, Fuzzy Modeling, Hierarchical ANN, Inference and Recognition, Information and Optimization, Interacting with The Brain, Machine Learning (ML), ML for Bio Medical systems, ML and Video-Image Processing, ML and Forensics, ML and Cybersecurity, ML and Social Media, ML in Engineering, Movement and Motion Detection, Multilayer Perceptrons and Kernel Networks, Natural Language, Object and Face Recognition, Recurrent Neural Networks and Reservoir Computing, Reinforcement Learning, Reservoir Computing, Self-Organizing Maps, Spiking Dynamics/Spiking ANN, Support Vector Machines, Swarm Intelligence and Decision-Making, Text Mining, Theoretical Neural Computation, Time Series and Forecasting, Training and Learning.

Intelligent Techniques for Planning

Author: Ioannis Vlahavas,Dimitris Vrakas

Publisher: IGI Global

ISBN: 1591404525

Category: Computers

Page: 364

View: 7710

The Intelligent Techniques for Planning presents a number of modern approaches to the area of automated planning. These approaches combine methods from classical planning such as the construction of graphs and the use of domain-independent heuristics with techniques from other areas of artificial intelligence. This book discuses, in detail, a number of state-of-the-art planning systems that utilize constraint satisfaction techniques in order to deal with time and resources, machine learning in order to utilize experience drawn from past runs, methods from knowledge systems for more expressive representation of knowledge and ideas from other areas such as Intelligent Agents. Apart from the thorough analysis and implementation details, each chapter of the book also provides extensive background information about its subject and presents and comments on similar approaches done in the past.

Encyclopedia of Machine Learning and Data Mining, Sammut & Webb, 2nd Ed, 2017

Encyclopedia of Machine Learning and Data Mining

Author: Springer Science, Inc

Publisher: Bukupedia


Category: Computers

Page: 1341

View: 4970

Machine learning and data mining are rapidly developing fields. Following the success of the first edition of the Encyclopedia of Machine Learning, we are delighted to bring you this updated and expanded edition. We have expanded the scope, as reflected in the revised title Encyclopedia of Machine Learning and Data Mining, to encompass more of the broader activity that surrounds the machine learning process. This includes new articles in such diverse areas as anomaly detection, online controlled experiments, and record linkage as well as substantial expansion of existing entries such as data preparation. We have also included new entries on key recent developments in core machine learning, such as deep learning. A thorough review has also led to updating of much of the existing content. This substantial tome is the product of an intense effort by many individuals. We thank the Editorial Board and the numerous contributors who have provided the content.We are grateful to the Springer team of Andrew Spencer, Michael Hermann, and Melissa Fearon who have shepherded us through the long process of bringing this second edition to print. We are also grateful to the production staff who have turned the content into its final form. We are confident that this revised encyclopedia will consolidate the first edition’s place as a key reference source for the machine learning and data mining communities.

Enterprise Business Modeling, Optimization Techniques, and Flexible Information Systems

Author: Papajorgji, Petraq

Publisher: IGI Global

ISBN: 1466639474

Category: Computers

Page: 252

View: 4645

Many factors can impact large-scale enterprise management systems, and maintaining these systems can be a complicated and challenging process. Therefore, businesses can benefit from an assortment of models and management styles to track and collect data for processes. Enterprise Business Modeling, Optimization Techniques, and Flexible Information Systems supplies a wide array of research on the intersections of business modeling, information systems, and optimization techniques. These various business models and structuring methods are proposed to provide ideas, methods, and points of view for managers, practitioners, entrepreneurs, and researchers on how to improve business processes.

Machine Learning: ECML-95

8th European Conference on Machine Learning, Heraclion, Crete, Greece, April 25 - 27, 1995. Proceedings

Author: Nada Lavrač,Stefan Wrobel

Publisher: Springer Science & Business Media

ISBN: 9783540592860

Category: Computers

Page: 370

View: 7682

This volume constitutes the proceedings of the Eighth European Conference on Machine Learning ECML-95, held in Heraclion, Crete in April 1995. Besides four invited papers the volume presents revised versions of 14 long papers and 26 short papers selected from a total of 104 submissions. The papers address all current aspects in the area of machine learning; also logic programming, planning, reasoning, and algorithmic issues are touched upon.

Machine Learning: ECML 2005

16th European Conference on Machine Learning, Porto, Portugal, October 3-7, 2005, Proceedings

Author: João Gama,Rui Camacho,Pavel Brazdil,Alípio Jorge,Luís Torgo

Publisher: Springer Science & Business Media

ISBN: 3540292438

Category: Computers

Page: 769

View: 6069

This book constitutes the refereed proceedings of the 16th European Conference on Machine Learning, ECML 2005, jointly held with PKDD 2005 in Porto, Portugal, in October 2005. The 40 revised full papers and 32 revised short papers presented together with abstracts of 6 invited talks were carefully reviewed and selected from 335 papers submitted to ECML and 30 papers submitted to both, ECML and PKDD. The papers present a wealth of new results in the area and address all current issues in machine learning.

Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics

8th European Conference, EvoBIO 2010, Istanbul, Turkey, April 7-9, 2010, Proceedings

Author: Clara Pizzuti,Marylyn D. Ritchie,Mario Giacobini

Publisher: Springer Science & Business Media

ISBN: 3642122108

Category: Computers

Page: 249

View: 3888

The ?eld of bioinformatics has two main objectives: the creation and main- nance of biological databases, and the discovery of knowledge from life sciences datainordertounravelthemysteriesofbiologicalfunction,leadingtonewdrugs andtherapiesforhumandisease. Life sciencesdatacomeinthe formofbiological sequences, structures, pathways, or literature. One major aspect of discovering biological knowledge is to search, predict, or model speci?c information in a given dataset in order to generate new interesting knowledge. Computer science methods such as evolutionary computation, machine learning, and data mining all have a great deal to o?er the ?eld of bioinformatics. The goal of the 8th - ropean Conference on Evolutionary Computation, Machine Learning, and Data Mining in Bioinformatics (EvoBIO 2010) was to bring together experts in these ?elds in order to discuss new and novel methods for tackling complex biological problems. The 8th EvoBIO conference was held in Istanbul, Turkey during April 7–9, 2010attheIstanbulTechnicalUniversity. EvoBIO2010washeldjointlywiththe 13th European Conference on Genetic Programming (EuroGP 2010), the 10th European Conference on Evolutionary Computation in Combinatorial Opti- sation (EvoCOP 2010), and the conference on the applications of evolutionary computation,EvoApplications. Collectively,the conferences areorganizedunder the name Evo* (www. evostar. org). EvoBIO, held annually as a workshop since 2003, became a conference in 2007 and it is now the premiere European event for those interested in the interface between evolutionary computation, machine learning, data mining, bioinformatics, and computational biology.

Data Analytics for Renewable Energy Integration

Second ECML PKDD Workshop, DARE 2014, Nancy, France, September 19, 2014, Revised Selected Papers

Author: Wei Lee Woon,Zeyar Aung,Stuart Madnick

Publisher: Springer

ISBN: 3319132903

Category: Computers

Page: 151

View: 7305

This book constitutes revised selected papers from the second ECML PKDD Workshop on Data Analytics for Renewable Energy Integration, DARE 2014, held in Nancy, France, in September 2014. The 11 papers presented in this volume were carefully reviewed and selected for inclusion in this book.

Machine Learning in Medical Imaging

5th International Workshop, MLMI 2014, Held in Conjunction with MICCAI 2014, Boston, MA, USA, September 14, 2014, Proceedings

Author: Guorong Wu,Daoqiang Zhang,Luping Zhou

Publisher: Springer

ISBN: 3319105817

Category: Computers

Page: 332

View: 8467

This book constitutes the refereed proceedings of the 5th International Workshop on Machine Learning in Medical Imaging, MLMI 2014, held in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2014, in Cambridge, MA, USA, in September 2014. The 40 contributions included in this volume were carefully reviewed and selected from 70 submissions. They focus on major trends and challenges in the area of machine learning in medical imaging and aim to identify new cutting-edge techniques and their use in medical imaging.

New Advances in Intelligence and Security Informatics

Author: Wenji Mao,FeiYue Wang

Publisher: Academic Press

ISBN: 0123973244

Category: Computers

Page: 116

View: 9654

The Intelligent Systems Series comprises titles that present state of the art knowledge and the latest advances in intelligent systems. Its scope includes theoretical studies, design methods, and real-world implementations and applications. Traditionally, Intelligence and Security Informatics (ISI) research and applications have focused on information sharing and data mining, social network analysis, infrastructure protection and emergency responses for security informatics. With the continuous advance of IT technologies and the increasing sophistication of national and international security, in recent years, new directions in ISI research and applications have emerged to address complicated problems with advanced technologies. This book provides a comprehensive and interdisciplinary account of the new advances in ISI area along three fundamental dimensions: methodological issues in security informatics; new technological developments to support security-related modeling, detection, analysis and prediction; and applications and integration in interdisciplinary socio-cultural fields. Identifies emerging directions in ISI research and applications that address the research challenges with advanced technologies Provides an integrated account of the new advances in ISI field in three core aspects: methodology, technological developments and applications Benefits researchers as well as security professionals who are involved in cutting-edge research and applications in security informatics and related fields

Recent Advances in Reinforcement Learning

Author: Leslie Pack Kaelbling

Publisher: Springer Science & Business Media

ISBN: 0792397053

Category: Computers

Page: 292

View: 5748

Recent Advances in Reinforcement Learning addresses current research in an exciting area that is gaining a great deal of popularity in the Artificial Intelligence and Neural Network communities. Reinforcement learning has become a primary paradigm of machine learning. It applies to problems in which an agent (such as a robot, a process controller, or an information-retrieval engine) has to learn how to behave given only information about the success of its current actions. This book is a collection of important papers that address topics including the theoretical foundations of dynamic programming approaches, the role of prior knowledge, and methods for improving performance of reinforcement-learning techniques. These papers build on previous work and will form an important resource for students and researchers in the area. Recent Advances in Reinforcement Learning is an edited volume of peer-reviewed original research comprising twelve invited contributions by leading researchers. This research work has also been published as a special issue of Machine Learning (Volume 22, Numbers 1, 2 and 3).

Machine Learning

Hands-On for Developers and Technical Professionals

Author: Jason Bell

Publisher: John Wiley & Sons

ISBN: 1118889495

Category: Mathematics

Page: 408

View: 615

Dig deep into the data with a hands-on guide to machinelearning Machine Learning: Hands-On for Developers and TechnicalProfessionals provides hands-on instruction and fully-codedworking examples for the most common machine learning techniquesused by developers and technical professionals. The book contains abreakdown of each ML variant, explaining how it works and how it isused within certain industries, allowing readers to incorporate thepresented techniques into their own work as they follow along. Acore tenant of machine learning is a strong focus on datapreparation, and a full exploration of the various types oflearning algorithms illustrates how the proper tools can help anydeveloper extract information and insights from existing data. Thebook includes a full complement of Instructor's Materials tofacilitate use in the classroom, making this resource useful forstudents and as a professional reference. At its core, machine learning is a mathematical, algorithm-basedtechnology that forms the basis of historical data mining andmodern big data science. Scientific analysis of big data requires aworking knowledge of machine learning, which forms predictionsbased on known properties learned from training data. MachineLearning is an accessible, comprehensive guide for thenon-mathematician, providing clear guidance that allows readersto: Learn the languages of machine learning including Hadoop,Mahout, and Weka Understand decision trees, Bayesian networks, and artificialneural networks Implement Association Rule, Real Time, and Batch learning Develop a strategic plan for safe, effective, and efficientmachine learning By learning to construct a system that can learn from data,readers can increase their utility across industries. Machinelearning sits at the core of deep dive data analysis andvisualization, which is increasingly in demand as companiesdiscover the goldmine hiding in their existing data. For the techprofessional involved in data science, Machine Learning:Hands-On for Developers and Technical Professionals providesthe skills and techniques required to dig deeper.

AI 2003: Advances in Artificial Intelligence

16th Australian Conference on AI, Perth, Australia, December 3-5, 2003, Proceedings

Author: Tamas D. Gedeon,Lance C.C. Fung

Publisher: Springer Science & Business Media

ISBN: 3540206469

Category: Computers

Page: 1075

View: 6834

This book constitutes the refereed proceedings of the 16th Australian Conference on Artificial Intelligence, AI 2003, held in Perth, Australia in December 2003. The 87 revised full papers presented together with 4 keynote papers were carefully reviewed and selected from 179 submissions. The papers are organized in topical sections on ontologies, problem solving, knowledge discovery and data mining, expert systems, neural network applications, belief revision and theorem proving, reasoning and logic, machine learning, AI applications, neural computing, intelligent agents, computer vision, medical applications, machine learning and language, AI and business, soft computing, language understanding, and theory.

A Concise Introduction to Models and Methods for Automated Planning

Author: Hector Geffner,Blai Bonet

Publisher: Morgan & Claypool Publishers

ISBN: 1608459705

Category: Computers

Page: 141

View: 4884

Planning is the model-based approach to autonomous behavior where the agent behavior is derived automatically from a model of the actions, sensors, and goals. The main challenges in planning are computational as all models, whether featuring uncertainty and feedback or not, are intractable in the worst case when represented in compact form. In this book, we look at a variety of models used in AI planning, and at the methods that have been developed for solving them. The goal is to provide a modern and coherent view of planning that is precise, concise, and mostly self-contained, without being shallow. For this, we make no attempt at covering the whole variety of planning approaches, ideas, and applications, and focus on the essentials. The target audience of the book are students and researchers interested in autonomous behavior and planning from an AI, engineering, or cognitive science perspective. Table of Contents: Preface / Planning and Autonomous Behavior / Classical Planning: Full Information and Deterministic Actions / Classical Planning: Variations and Extensions / Beyond Classical Planning: Transformations / Planning with Sensing: Logical Models / MDP Planning: Stochastic Actions and Full Feedback / POMDP Planning: Stochastic Actions and Partial Feedback / Discussion / Bibliography / Author's Biography

Manufacturing Systems and Technologies for the New Frontier

The 41st CIRP Conference on Manufacturing Systems May 26–28, 2008, Tokyo, Japan

Author: Mamoru Mitsuishi,Kanji Ueda,Fumihiko Kimura

Publisher: Springer Science & Business Media

ISBN: 184800267X

Category: Technology & Engineering

Page: 556

View: 1332

Collected here are 112 papers concerned with all manner of new directions in manufacturing systems given at the 41st CIRP Conference on Manufacturing Systems. The high-quality material presented in this volume includes reports of work from both scientific and engineering standpoints and several invited and keynote papers addressing the current cutting edge and likely future trends in manufacturing systems. The book’s subjects include: (1) new trends in manufacturing systems design: sustainable design, ubiquitous manufacturing, emergent synthesis, service engineering, value creation, cost engineering, human and social aspects of manufacturing, etc.; (2) new applications for manufacturing systems – medical, life-science, optics, NEMS, etc.; (3) intelligent use of advanced methods and new materials – new manufacturing process technologies, high-hardness materials, bio-medical materials, etc.; (4) integration and control for new machines – compound machine tools, rapid prototyping, printing process integration, etc.

Applications of Learning & Planning Methods

Author: Nikolaos G. Bourbakis

Publisher: World Scientific

ISBN: 9789810205461

Category: Computers

Page: 365

View: 3469

Learning and planning are two important topics of artificial intelligence. Learning deals with the algorithmic processes that make a computing machine able to ?learn? and improve its performance during the process of complex tasks. Planning on the other hand, deals with decision and construction processes that make a machine capable of constructing an intelligent plan for the solution of a particular complex problem.This book combines both learning and planning methodologies and their applications in different domains. It is divided into two parts. The first part contains seven chapters on the ongoing research work in symbolic and connectionist learning. The second part includes seven chapters which provide the current research efforts in planning methodologies and their application to robotics.

Artificial Intelligence for Advanced Problem Solving Techniques

Author: Vlahavas, Ioannis

Publisher: IGI Global

ISBN: 9781599047072

Category: Education

Page: 388

View: 8412

One of the most important functions of artificial intelligence, automated problem solving, consists mainly of the development of software systems designed to find solutions to problems. These systems utilize a search space and algorithms in order to reach a solution. Artificial Intelligence for Advanced Problem Solving Techniques offers scholars and practitioners cutting-edge research on algorithms and techniques such as search, domain independent heuristics, scheduling, constraint satisfaction, optimization, configuration, and planning, and highlights the relationship between the search categories and the various ways a specific application can be modeled and solved using advanced problem solving techniques.