For some time, all branches of the military have used a wide range of sensors to provide data for many purposes, including surveillance, reconnoitring, target detection and battle damage assessment. Many nations have also attempted to utilise these sensors for civilian applications, such as crop monitoring, agricultural disease tracking, environmental diagnostics, cartography, ocean temperature profiling, urban planning, and the characterisation of the Ozone Hole above Antarctica. The recent convergence of several important technologies has made possible new, advanced, high performance, sensor based applications relying on the near-simultaneous fusion of data from an ensemble of different types of sensors. The book examines the underlying principles of sensor operation and data fusion, the techniques and technologies that enable the process, including the operation of 'fusion engines'. Fundamental theory and the enabling technologies of data fusion are presented in a systematic and accessible manner. Applications are discussed in the areas of medicine, meteorology, BDA and targeting, transportation, cartography, the environment, agriculture, and manufacturing and process control.

This book constitutes the refereed proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2002, held in Manchester, UK in August 2002.The 89 revised papers presented were carefully reviewed and selected from more than 150 submissions. The book offers topical sections on data mining, knowledge engineering, text and document processing, internet applications, agent technology, autonomous mining, financial engineering, bioinformatics, learning systems, and pattern recognition.

Effective decision-making while trading off the constraints and conflicting multiple objectives under rapid technological developments, massive generation of data, and extreme volatility is of paramount importance to organizations to win over the time-based competition today. While agility is a crucial issue, the firms have been increasingly relying on evidence-based decision-making through intelligent decision support systems driven by computational intelligence and automation to achieve a competitive advantage.  The decisions are no longer confined to a specific functional area. Instead, business organizations today find actionable insight for formulating future courses of action by integrating multiple objectives and perspectives. Therefore, multi-objective decision-making plays a critical role in businesses and industries. In this regard, the importance of Operations Research (OR) models and their applications enables the firms to derive optimum solutions subject to various constraints and/or objectives while considering multiple functional areas of the organizations together. Hence, researchers and practitioners have extensively applied OR models to solve various organizational issues related to manufacturing, service, supply chain and logistics management, human resource management, finance, and market analysis, among others. Further, OR models driven by AI have been enabled to provide intelligent decision-support frameworks for achieving sustainable development goals. The present issue provides a unique platform to showcase the contributions of the leading international experts on production systems and business from academia, industry, and government to discuss the issues in intelligent manufacturing, operations management, financial management, supply chain management, and Industry 4.0 in the Artificial Intelligence era. Some of the general (but not specific) scopes of this proceeding entail OR models such as Optimization and Control, Combinatorial Optimization, Queuing Theory, Resource Allocation Models, Linear and Nonlinear Programming Models, Multi-objective and multi-attribute Decision Models, Statistical Quality Control along with AI, Bayesian Data Analysis, Machine Learning and Econometrics and their applications vis-à-vis AI & Data-driven Production Management, Marketing and Retail Management, Financial Management, Human Resource Management, Operations Management, Smart Manufacturing & Industry 4.0, Supply Chain and Logistics Management, Digital Supply Network, Healthcare Administration, Inventory Management, consumer behavior, security analysis, and portfolio management and sustainability.   The present issue shall be of interest to the faculty members, students, and scholars of various engineering and social science institutions and universities, along with the practitioners and policymakers of different industries and organizations.

This book constitutes the refereed proceedings of the Third International Workshop on Ant Algorithms, ANTS 2002, held in Brussels, Belgium in September 2002.The 17 revised full papers, 11 short papers, and extended poster abstracts presented were carefully reviewed and selected from 52 submissions. The papers deal with theoretical and foundational aspects and a variety of new variants of ant algorithms as well as with a broad variety of optimization applications in networking and operations research. All in all, this book presents the state of the art in research and development in the emerging field of ant algorithms

This book constitutes the refereed proceedings of the 15th Annual Conference on Computational Learning Theory, COLT 2002, held in Sydney, Australia, in July 2002.The 26 revised full papers presented were carefully reviewed and selected from 55 submissions. The papers are organized in topical sections on statistical learning theory, online learning, inductive inference, PAC learning, boosting, and other learning paradigms.

This book constitutes the thoroughly refereed post-proceedings of the 4th International Workshop on Learning Classifier Systems, IWLCS 2001, held in San Francisco, CA, USA, in July 2001.The 12 revised full papers presented together with a special paper on a formal description of ACS have gone through two rounds of reviewing and improvement. The first part of the book is devoted to theoretical issues of learning classifier systems including the influence of exploration strategy, self-adaptive classifier systems, and the use of classifier systems for social simulation. The second part is devoted to applications in various fields such as data mining, stock trading, and power distributionn networks.

This book constitutes the refereed proceedings of the Third International Workshop on Multiple Classifier Systems, MCS 2002, held in Cagliari, Italy, in June 2002.The 29 revised full papers presented together with three invited papers were carefully reviewed and selected for inclusion in the volume. The papers are organized in topical sections on bagging and boosting, ensemble learning and neural networks, design methodologies, combination strategies, analysis and performance evaluation, and applications.

Machine learning applications autonomously reason about data at massive scale. It's important that they remain responsive in the face of failure and changes in load. But machine learning systems are different than other applications when it comes to testing, building, deploying, and monitoring. Reactive Machine Learning Systems teaches readers how to implement reactive design solutions in their machine learning systems to make them as reliable as a well-built web app. Using Scala and powerful frameworks such as Spark, MLlib, and Akka, they'll learn to quickly and reliably move from a single machine to a massive cluster. Key Features: * Example-rich guide * Step-by-step guide * Move from single-machine to massive cluster Readers should have intermediate skills in Java or Scala. No previous machine learning experience is required. About the Technology: Machine learning systems are different than other applications when it comes to testing, building, deploying, and monitoring. To make machine learning systems reactive, you need to understand both reactive design patterns and modern data architecture patterns.

This volume contains the papers presented at the 12th Annual Conference on Algorithmic Learning Theory (ALT 2001), which was held in Washington DC, USA, during November 25-28, 2001. The main objective of the conference is to provide an inter-disciplinary forum for the discussion of theoretical foundations of machine learning, as well as their relevance to practical applications. The conference was co-located with the Fourth International Conference on Discovery Science (DS 2001). The volume includes 21 contributed papers. These papers were selected by the program committee from 42 submissions based on clarity, signi?cance, o- ginality, and relevance to theory and practice of machine learning. Additionally, the volume contains the invited talks of ALT 2001 presented by Dana Angluin of Yale University, USA, Paul R. Cohen of the University of Massachusetts at Amherst, USA, and the joint invited talk for ALT 2001 and DS 2001 presented by Setsuo Arikawa of Kyushu University, Japan. Furthermore, this volume includes abstracts of the invited talks for DS 2001 presented by Lindley Darden and Ben Shneiderman both of the University of Maryland at College Park, USA. The complete versions of these papers are published in the DS 2001 proceedings (Lecture Notes in Arti?cial Intelligence Vol. 2226).

This book constitutes the refereed proceedings of the 11th International Conference on Inductive Logic Programming, ILP 2001, held in Strasbourg, France in September 2001.The 21 revised full papers presented were carefully reviewed and selected from 37 submissions. Among the topics addressed are data mining issues for multi-relational databases, supervised learning, inductive inference, Bayesian reasoning, learning refinement operators, neural network learning, constraint satisfaction, genetic algorithms, statistical machine learning, transductive inference, etc.

This book presents the thoroughly refereed post-proceedings of the Second International Workshop on Intelligent Memory Systems, IMS 2000, held in Cambridge, MA, USA, in November 2000.The nine revised full papers and six poster papers presented were carefully reviewed and selected from 28 submissions. The papers cover a wide range of topics in intelligent memory computing; they are organized in topical sections on memory technology, processor and memory architecture, applications and operating systems, and compiler technology.

This book constitutes the refereed proceedings of the 12th European Conference on Machine Learning, ECML 2001, held in Freiburg, Germany, in September 2001.The 50 revised full papers presented together with four invited contributions were carefully reviewed and selected from a total of 140 submissions. Among the topics covered are classifier systems, naive-Bayes classification, rule learning, decision tree-based classification, Web mining, equation discovery, inductive logic programming, text categorization, agent learning, backpropagation, reinforcement learning, sequence prediction, sequential decisions, classification learning, sampling, and semi-supervised learning.

In recent years machine learning has made its way from artificial intelligence into areas of administration, commerce, and industry. Data mining is perhaps the most widely known demonstration of this migration, complemented by less publicized applications of machine learning like adaptive systems in industry, financial prediction, medical diagnosis and the construction of user profiles for Web browsers.This book presents the capabilities of machine learning methods and ideas on how these methods could be used to solve real-world problems. The first ten chapters assess the current state of the art of machine learning, from symbolic concept learning and conceptual clustering to case-based reasoning, neural networks, and genetic algorithms. The second part introduces the reader to innovative applications of ML techniques in fields such as data mining, knowledge discovery, human language technology, user modeling, data analysis, discovery science, agent technology, finance, etc.

This book is for developers who are looking for an introduction to basic concepts in NLP and machine learning. Numerous code samples and listings are included to support myriad topics. The first two chapters contain introductory material for NumPy and Pandas, followed by chapters on NLP concepts, algorithms and toolkits, machine learning, and NLP applications. The final chapters include examples of NLP tasks using TF2 and Keras, the Transformer architecture, BERT-based models, and the GPT family of models. The appendices contain introductory material (including Python code samples) for various topics, including data and statistics, Python3, regular expressions, Keras, TF2, Matplotlib and Seaborn. Companion files with source code and figures are included. FEATURES * Covers extensive topics related to natural language processing and machine learning * Includes separate appendices on data and statistics, regular expressions, data visualization, Python, Keras, TF2, and more * Features companion files with source code and color figures from the book

Learning classi er systems are rule-based systems that exploit evolutionary c- putation and reinforcement learning to solve di cult problems. They were - troduced in 1978 by John H. Holland, the father of genetic algorithms, and since then they have been applied to domains as diverse as autonomous robotics, trading agents, and data mining. At the Second International Workshop on Learning Classi er Systems (IWLCS 99), held July 13, 1999, in Orlando, Florida, active researchers reported on the then current state of learning classi er system research and highlighted some of the most promising research directions. The most interesting contri- tions to the meeting are included in the book Learning Classi er Systems: From Foundations to Applications, published as LNAI 1813 by Springer-Verlag. The following year, the Third International Workshop on Learning Classi er Systems (IWLCS 2000), held September 15{16 in Paris, gave participants the opportunity to discuss further advances in learning classi er systems. We have included in this volume revised and extended versions of thirteen of the papers presented at the workshop.

Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning-especially deep neural networks-make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks. Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you'll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J. Dive into machine learning concepts in general, as well as deep learning in particular Understand how deep networks evolved from neural network fundamentals Explore the major deep network architectures, including Convolutional and Recurrent Learn how to map specific deep networks to the right problem Walk through the fundamentals of tuning general neural networks and specific deep network architectures Use vectorization techniques for different data types with DataVec, DL4J's workflow tool Learn how to use DL4J natively on Spark and Hadoop

This book builds upon the foundations established in its first edition, with updated chapters and the latest code implementations to bring it up to date with Tensorflow 2.0. Pro Deep Learning with TensorFlow 2.0 begins with the mathematical and core technical foundations of deep learning. Next, you will learn about convolutional neural networks, including new convolutional methods such as dilated convolution, depth-wise separable convolution, and their implementation. You'll then gain an understanding of natural language processing in advanced network architectures such as transformers and various attention mechanisms relevant to natural language processing and neural networks in general. As you progress through the book, you'll explore unsupervised learning frameworks that reflect the current state of deep learning methods, such as autoencoders and variational autoencoders. The final chapter covers the advanced topic of generative adversarial networks and their variants, such as cycle consistency GANs and graph neural network techniques such as graph attention networks and GraphSAGE. Upon completing this book, you will understand the mathematical foundations and concepts of deep learning, and be able to use the prototypes demonstrated to build new deep learning applications. What You Will Learn Understand full-stack deep learning using TensorFlow 2.0 Gain an understanding of the mathematical foundations of deep learning Deploy complex deep learning solutions in production using TensorFlow 2.0 Understand generative adversarial networks, graph attention networks, and GraphSAGE Who This Book Is For: Data scientists and machine learning professionals, software developers, graduate students, and open source enthusiasts.

Driven by the requirements of a large number of practical and commercially - portant applications, the last decade has witnessed considerable advances in p- tern recognition. Better understanding of the design issues and new paradigms, such as the Support Vector Machine, have contributed to the development of - proved methods of pattern classi cation. However, while any performance gains are welcome, and often extremely signi cant from the practical point of view, it is increasingly more challenging to reach the point of perfection as de ned by the theoretical optimality of decision making in a given decision framework. The asymptoticity of gains that can be made for a single classi er is a re?- tion of the fact that any particular design, regardless of how good it is, simply provides just one estimate of the optimal decision rule. This observation has motivated the recent interest in Multiple Classi er Systems , which aim to make use of several designs jointly to obtain a better estimate of the optimal decision boundary and thus improve the system performance. This volume contains the proceedings of the international workshop on Multiple Classi er Systems held at Robinson College, Cambridge, United Kingdom (July 2{4, 2001), which was organized to provide a forum for researchers in this subject area to exchange views and report their latest results.

This volume contains papers presented at the joint 14th Annual Conference on Computational Learning Theory and 5th European Conference on Computat- nal Learning Theory, held at the Trippenhuis in Amsterdam, The Netherlands from July 16 to 19, 2001. The technical program contained 40 papers selected from 69 submissions. In addition, David Stork (Ricoh California Research Center) was invited to give an invited lecture and make a written contribution to the proceedings. The Mark Fulk Award is presented annually for the best paper co-authored by a student. This year's award was won by Olivier Bousquet for the paper "Tracking a Small Set of Modes by Mixing Past Posteriors" (co-authored with Manfred K. Warmuth). We gratefully thank all of the individuals and organizations responsible for the success of the conference. We are especially grateful to the program c- mittee: Dana Angluin (Yale), Peter Auer (Univ. of Technology, Graz), Nello Christianini (Royal Holloway), Claudio Gentile (Universit'a di Milano), Lisa H- lerstein (Polytechnic Univ.), Jyrki Kivinen (Univ. of Helsinki), Phil Long (- tional Univ. of Singapore), Manfred Opper (Aston Univ.) , John Shawe-Taylor (Royal Holloway), Yoram Singer (Hebrew Univ.), Bob Sloan (Univ. of Illinois at Chicago), Carl Smith (Univ. of Maryland), Alex Smola (Australian National Univ.), and Frank Stephan (Univ. of Heidelberg), for their e?orts in reviewing and selecting the papers in this volume.

This book constitutes the refereed proceedings of the 4th European Conference on Genetic Programming, EuroGP 2001, held at Lake Como, Italy in April 2001.The 17 revised full papers and 13 research posters presented were carefully reviewed and selected during a rigorous double-blind refereeing process out of 42 submissions. All current aspects of genetic programming are addressed, ranging from theoretical and foundational issues to applications in a variety of fields such as robotics, artificial retina, character recognition, financial prediction, digital filter and electronic circuit design, image processing, data fusion, and bio-sequencing.

Learn to understand and implement the latest machine learning innovations to improve your investment performance Machine learning (ML) is changing virtually every aspect of our lives. Today, ML algorithms accomplish tasks that - until recently - only expert humans could perform. And finance is ripe for disruptive innovations that will transform how the following generations understand money and invest. In the book, readers will learn how to: Structure big data in a way that is amenable to ML algorithms Conduct research with ML algorithms on big data Use supercomputing methods and back test their discoveries while avoiding false positives Advances in Financial Machine Learning addresses real life problems faced by practitioners every day, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their individual setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.

The practical application of Genetic Algorithms to the solution of engineering problems, is rapidly becoming an established approach in the fields of control and signal processing. This book provides comprehensive coverage of the techniques involved, describing the intrinsic characteristics, advantages and constraints of genetic algorithms, as well as discussing genetic operations such as crossover, mutation and reinsertion. In addition, the principle of multiobjective optimization and computing parallelism are discussed. These features are fully illustrated by real-world applications. Also described is a newly proposed and unique hierarchical genetic algorithm designed to address the problems in determining system topology. For added value, a 3.5" disk accompanies the book, that provides the reader with an interactive Genetic Algorithms demonstration programme.

Sequential behavior is essential to intelligence in general and a fundamental part of human activities, ranging from reasoning to language, and from everyday skills to complex problem solving. Sequence learning is an important component of learning in many tasks and application fields: planning, reasoning, robotics natural language processing, speech recognition, adaptive control, time series prediction, financial engineering, DNA sequencing, and so on. This book presents coherently integrated chapters by leading authorities and assesses the state of the art in sequence learning by introducing essential models and algorithms and by examining a variety of applications. The book offers topical sections on sequence clustering and learning with Markov models, sequence prediction and recognition with neural networks, sequence discovery with symbolic methods, sequential decision making, biologically inspired sequence learning models.

This volume contains all the papers presented at the Eleventh International C- ference on Algorithmic Learning Theory (ALT 2000) held at Coogee Holiday Inn, Sydney, Australia,11-13 December 2000. The conference was sponsored by the School of Computer Science and Engineering, University of New South Wales, and supported by the IFIP Working Group 1.4 on Computational Learning T- ory and the Computer Science Association (CSA) of Australia. In response to the call for papers 39 submissions were received on all aspects of algorithmic learning theory. Out of these 22 papers were accepted for p- sentation by the program committee. In addition, there were three invited talks by William Cohen (Whizbang Labs), Tom Dietterich (Oregon State Univeristy), and Osamu Watanabe (Tokyo Institute of Technology). This year's conference is the last in the millenium and eleventh overall in the ALT series. The ?rst ALT workshop was held in Tokyo in 1990. It was merged with the workshop on Analogical and Inductive Inference in 1994. The conf- ence focuses on all areas related to algorithmic learning theory, including (but not limited to) the design and analysis of learning algorithms, the theory of machine learning, computational logic of/for machine discovery, inductive inf- ence, learning via queries, new learning models, scienti?c discovery, learning by analogy, arti?cial and biological neural networks, pattern recognition, statistical learning, Bayesian/MDL estimation, inductive logic programming, data m- ing and knowledge discovery, and application of learning to biological sequence analysis. In the current conference there were papers from a variety of the above areas, refelecting both the theoretical as well as practical aspec

This volumecontains the papers presentedatthe SeventhInternationalC- ference on Logicfor Programmingand Automated Reasoning (LPAR 2000)held onReunionIsland, France,6 10November2000, followedbythe ReunionWo- shop on Implementation of Logic. Sixty-?ve papers were submitted to LPAR 2000 of which twenty-six papers were accepted. Submissions by the program committee members were not - lowed. There was a special category of experimental papers intended to describe implementations of systems, to report experiments with implemented systems, orto compareimplementedsystems.Eachof thesubmissionswasreviewedbyat least three program committee members and an electronic program committee meeting was held via the Internet. In addition to the refereed papers, this volume contains full papers by two of the four invited speakers, Georg Gottlob and Micha] el Rusinowitch, along with an extended abstract of Bruno Courcelle s invited lecture and an abstract of Erich Gr] adel s invited lecture. WewouldliketothankthemanypeoplewhohavemadeLPAR2000possible. We are grateful to the following groups and individuals: the program and or- nizing committees; the additional referees; the local arrangements chair Teodor Knapik; PascalManoury, who was in chargeof accommodation; Konstantin - rovin, whomaintainedthe programcommittee Webpage;andBillMcCune, who implemented the program committee management software."