This book constitutes the refereed proceedings of the 6th International Workshop on Algorithmic Learning Theory, ALT '95, held in Fukuoka, Japan, in October 1995.
The book contains 21 revised full papers selected from 46 submissions together with three invited contributions. It covers all current areas related to algorithmic learning theory, in particular the theory of machine learning, design and analysis of learning algorithms, computational logic aspects, inductive inference, learning via queries, artificial and biologicial neural network learning, pattern recognition, learning by analogy, statistical learning, inductive logic programming, robot learning, and gene analysis.

This book is the final report on a comprehensive basic research project, named GOSLER on algorithmic learning for knowledge-based systems supported by the German Federal Ministry of Research and Technology during the years 1991 - 1994. This research effort was focused on the study of fundamental learnability problems integrating theoretical research with the development of tools and experimental investigation.
The contributions by 11 participants in the GOSLER project is complemented by contributions from 23 researchers from abroad. Thus the volume provides a competent introduction to algorithmic learning theory.

This book is based on the workshop on Adaptation and Learning in Multi-Agent Systems, held in conjunction with the International Joint Conference on Artificial Intelligence, IJCAI'95, in Montreal, Canada in August 1995.
The 14 thoroughly reviewed revised papers reflect the whole scope of current aspects in the field: they describe and analyze, both experimentally and theoretically, new learning and adaption approaches for situations in which several agents have to cooperate or compete. Also included, and aimed at the novice reader, are a comprehensive introductory survey on the area with 154 references listed and a subject index. As the first book solely devoted to this area, this volume documents the state of the art and is thus indispensable for anyone active or interested in the field.

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.

This volume presents the proceedings of the Second European Conference on Computational Learning Theory (EuroCOLT '95), held in Barcelona, Spain in March 1995.
The book contains full versions of the 28 papers accepted for presentation at the conference as well as three invited papers. All relevant topics in fundamental studies of computational aspects of artificial and natural learning systems and machine learning are covered; in particular artificial and biological neural networks, genetic and evolutionary algorithms, robotics, pattern recognition, inductive logic programming, decision theory, Bayesian/MDL estimation, statistical physics, and cryptography are addressed.

Die auf drei Bande angelegte Reihe mit prufungsrelevanten Aufgaben und Losungen erlautert grundlegende Mathematik-bezogene Methoden der Informatik. Der vorliegende erste Band "Induktives Vorgehen" intoniert das durch das Zusammenspiel von Struktur, Invarianz und Abstraktion gepragte Leitthema der Trilogie zu den "Grundlagen der Hoheren Informatik." Die beide Folgebande "Algebraisches Denken" und " Perfektes Modellieren" greifen dieses Thema dann variierend und in immer komplexer werdenden Zusammenhangen vertiefend auf. Wie beim Bolero von Ravel, wo die gleiche Melodie von immer mehr Musikern mit immer mehr Instrumenten gespielt wird, soll dies dazu fuhren, dass der Leser das Leitthema derart verinnerlicht, dass er es selbst an ungewohnter Stelle wiedererkennen und eigenstandig auf neue Szenarien ubertragen kann. Damit hat er beste Voraussetzungen fur das weitere Informatikstudium und eine erfolgreiche berufliche Zukunft, sei es in Wissenschaft, Management oder Industrie."

This volume presents the proceedings of the Second International Colloquium on Grammatical Inference (ICGI-94), held in Alicante, Spain in September 1994.
Besides 25 research papers carefully selected and refereed by the program committee, the book contains a survey by E. Vidal. The book is devoted to all those aspects of automatic learning that explicitly focus on principles, theory, and applications of grammars and languages. The papers are organized in sections on formal aspects; language modelling and linguistic applications; stochastic approaches, applications and performance analysis; and neural networks, genetic algorithms, and artificial intelligence techniques.

This volume presents the proceedings of the Fourth International Workshop on Analogical and Inductive Inference (AII '94) and the Fifth International Workshop on Algorithmic Learning Theory (ALT '94), held jointly at Reinhardsbrunn Castle, Germany in October 1994. (In future the AII and ALT workshops will be amalgamated and held under the single title of Algorithmic Learning Theory.)
The book contains revised versions of 45 papers on all current aspects of computational learning theory; in particular, algorithmic learning, machine learning, analogical inference, inductive logic, case-based reasoning, and formal language learning are addressed.

The central purpose of this book is to acquaint the reader especially with the cases of local search based learning as well as to introduce methods of constraint based reasoning, both with respect to their use in automated manufacturing. We restrict our attention to job shop scheduling as well as to one-machine scheduling with sequence dependent setup times. Additionally some design and planning issues in flexible manufacturing systems are considered. General purpose search methods which in particular include methods from local search such as simulated annealing, tabu search, and genetic algorithms, are the basic ingredients of the proposed intelligent knowledge-based scheduling systems, enriched by a number of constraint-based local decision rules in order to introduce problem specific knowledge.

The objective of this book is two-fold. Firstly, it is aimed at bringing to gether key research articles concerned with methodologies for knowledge discovery in databases and their applications. Secondly, it also contains articles discussing fundamentals of rough sets and their relationship to fuzzy sets, machine learning, management of uncertainty and systems of logic for formal reasoning about knowledge. Applications of rough sets in different areas such as medicine, logic design, image processing and expert systems are also represented. The articles included in the book are based on selected papers presented at the International Workshop on Rough Sets and Knowledge Discovery held in Banff, Canada in 1993. The primary methodological approach emphasized in the book is the mathematical theory of rough sets, a relatively new branch of mathematics concerned with the modeling and analysis of classification problems with imprecise, uncertain, or incomplete information. The methods of the theory of rough sets have applications in many sub-areas of artificial intelligence including knowledge discovery, machine learning, formal reasoning in the presence of uncertainty, knowledge acquisition, and others. This spectrum of applications is reflected in this book where articles, although centered around knowledge discovery problems, touch a number of related issues. The book is intended to provide an important reference material for students, researchers, and developers working in the areas of knowledge discovery, machine learning, reasoning with uncertainty, adaptive expert systems, and pattern classification."

With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help development teams produce models that are secure, more robust, less biased, and more explainable. Authors Yada Pruksachatkun, Matthew McAteer, and Subhabrata Majumdar translate best practices in the academic literature for curating datasets and building models into a blueprint for building industry-grade trusted ML systems. With this book, engineers and data scientists will gain a much-needed foundation for releasing trustworthy ML applications into a noisy, messy, and often hostile world. You'll learn: Methods to explain ML models and their outputs to stakeholders How to recognize and fix fairness concerns and privacy leaks in an ML pipeline How to develop ML systems that are robust and secure against malicious attacks Important systemic considerations, like how to manage trust debt and which ML obstacles require human intervention

This volume contains the proceedings of the European Conference on Machine Learning 1994, which continues the tradition of earlier meetings and which is a major forum for the presentation of the latest and most significant results in machine learning.
Machine learning is one of the most important subfields of artificial intelligence and computer science, as it is concerned with the automation of learning processes.
This volume contains two invited papers, 19 regular papers, and 25 short papers carefully reviewed and selected from in total 88 submissions.
The papers describe techniques, algorithms, implementations, and experiments in the area of machine learning.

This book offers a model for concepts and their dynamics. A basic assumptionis that concepts are composed of specified components, which are representedby large binary patterns whose psychological meaning is governed by the interaction between conceptual modules and other functional modules. A recurrent connectionist model is developed in which some inputs are attracted faster than others by an attractor, where convergence times can beinterpreted as decision latencies. The learning rule proposed is extracted from psychological experiments. The rule has the property that that whena context becomes more familiar, the associations between the concepts of the context spontaneously evolve from loose associations to a more taxonomicorganization.

This volume contains all the papers that were presented at the Fourth Workshop on Algorithmic Learning Theory, held in Tokyo in November 1993. In addition to 3 invited papers, 29 papers were selected from 47 submitted extended abstracts. The workshop was the fourth in a series of ALT workshops, whose focus is on theories of machine learning and the application of such theories to real-world learning problems. The ALT workshops have been held annually since 1990, sponsored by the Japanese Society for Artificial Intelligence. The volume is organized into parts on inductive logic and inference, inductive inference, approximate learning, query learning, explanation-based learning, and new learning paradigms.

Digital forensics deals with the acquisition, preservation, examination, analysis and presentation of electronic evidence. Computer networks, cloud computing, smartphones, embedded devices and the Internet of Things have expanded the role of digital forensics beyond traditional computer crime investigations. Practically every crime now involves some aspect of digital evidence; digital forensics provides the techniques and tools to articulate this evidence in legal proceedings. Digital forensics also has myriad intelligence applications; furthermore, it has a vital role in cyber security -- investigations of security breaches yield valuable information that can be used to design more secure and resilient systems.Advances in Digital Forensics XVII describes original research results and innovative applications in the discipline of digital forensics. In addition, it highlights some of the major technical and legal issues related to digital evidence and electronic crime investigations. The areas of coverage include: themes and issues, forensic techniques, filesystem forensics, cloud forensics, social media forensics, multimedia forensics, and novel applications. This book is the seventeenth volume in the annual series produced by the International Federation for Information Processing (IFIP) Working Group 11.9 on Digital Forensics, an international community of scientists, engineers and practitioners dedicated to advancing the state of the art of research and practice in digital forensics. The book contains a selection of thirteen edited papers from the Seventeenth Annual IFIP WG 11.9 International Conference on Digital Forensics, held virtually in the winter of 2021. Advances in Digital Forensics XVII is an important resource for researchers, faculty members and graduate students, as well as for practitioners and individuals engaged in research and development efforts for the law enforcement and intelligence communities.

The past decade has witnessed a wide adoption of artificial intelligence and machine learning (AI/ML) technologies. However, a lack of oversight into their widespread implementation has resulted in harmful outcomes that could have been avoided with proper oversight. Before we can realize AI/ML's true benefit, practitioners must understand how to mitigate its risks. This book describes responsible AI, a holistic approach for improving AI/ML technology, business processes, and cultural competencies that builds on best practices in risk management, cybersecurity, data privacy, and applied social science. It's an ambitious undertaking that requires a diverse set of talents, experiences, and perspectives. Data scientists and nontechnical oversight folks alike need to be recruited and empowered to audit and evaluate high-impact AI/ML systems. Author Patrick Hall created this guide for a new generation of auditors and assessors who want to make AI systems better for organizations, consumers, and the public at large. Learn how to create a successful and impactful responsible AI practice Get a guide to existing standards, laws, and assessments for adopting AI technologies Look at how existing roles at companies are evolving to incorporate responsible AI Examine business best practices and recommendations for implementing responsible AI Learn technical approaches for responsible AI at all stages of system development

Artificial neural networks and genetic algorithms both are areas of research which have their origins in mathematical models constructed in order to gain understanding of important natural processes. By focussing on the process models rather than the processes themselves, significant new computational techniques have evolved which have found application in a large number of diverse fields. This diversity is reflected in the topics which are the subjects of contributions to this volume. There are contributions reporting theoretical developments in the design of neural networks, and in the management of their learning. In a number of contributions, applications to speech recognition tasks, control of industrial processes as well as to credit scoring, and so on, are reflected. Regarding genetic algorithms, several methodological papers consider how genetic algorithms can be improved using an experimental approach, as well as by hybridizing with other useful techniques such as tabu search. The closely related area of classifier systems also receives a significant amount of coverage, aiming at better ways for their implementation. Further, while there are many contributions which explore ways in which genetic algorithms can be applied to real problems, nearly all involve some understanding of the context in order to apply the genetic algorithm paradigm more successfully. That this can indeed be done is evidenced by the range of applications covered in this volume.

This volume includes some of the key research papers in the area of machine learning produced at MIT and Siemens during a three-year joint research effort. It includes papers on many different styles of machine learning, organized into three parts. Part I, theory, includes three papers on theoretical aspects of machine learning. The first two use the theory of computational complexity to derive some fundamental limits on what isefficiently learnable. The third provides an efficient algorithm for identifying finite automata. Part II, artificial intelligence and symbolic learning methods, includes five papers giving an overview of the state of the art and future developments in the field of machine learning, a subfield of artificial intelligence dealing with automated knowledge acquisition and knowledge revision. Part III, neural and collective computation, includes five papers sampling the theoretical diversity and trends in the vigorous new research field of neural networks: massively parallel symbolic induction, task decomposition through competition, phoneme discrimination, behavior-based learning, and self-repairing neural networks.

This volume contains the proceedings of the Eurpoean Conference on Machine Learning (ECML-93), continuing the tradition of the five earlier EWSLs (European Working Sessions on Learning). The aim of these conferences is to provide a platform for presenting the latest results in the area of machine learning. The ECML-93 programme included invited talks, selected papers, and the presentation of ongoing work in poster sessions. The programme was completed by several workshops on specific topics. The volume contains papers related to all these activities. The first chapter of the proceedings contains two invited papers, one by Ross Quinlan and one by Stephen Muggleton on inductive logic programming. The second chapter contains 18 scientific papers accepted for the main sessions of the conference. The third chapter contains 18 shorter position papers. The final chapter includes three overview papers related to the ECML-93 workshops.

This volume contains the text of the five invited papers and 16 selected contributions presented at the third International Workshop on Analogical and Inductive Inference, AII 92, held in Dagstuhl Castle, Germany, October 5-9, 1992. Like the two previous events, AII '92 was intended to bring together representatives from several research communities, in particular, from theoretical computer science, artificial intelligence, and from cognitive sciences. The papers contained in this volume constitute a state-of-the-art report on formal approaches to algorithmic learning, particularly emphasizing aspects of analogical reasoning and inductive inference. Both these areas are currently attracting strong interest: analogical reasoning plays a crucial role in the booming field of case-based reasoning, and, in the fieldof inductive logic programming, there have recently been developed a number of new techniques for inductive inference.

This volume grew out of a workshop designed to bring together researchers from different fields and includes contributions from workers in Bayesian analysis, machine learning, neural nets, PAC and VC theory, classical sampling theory statistics and the statistical physics of learning. The contributions present a bird's-eye view of the subject.

Healthcare transformation requires us to continually look at new and better ways to manage insights - both within and outside the organization today. Increasingly, the ability to glean and operationalize new insights efficiently as a byproduct of an organization's day-to-day operations is becoming vital to hospitals and health systems ability to survive and prosper. One of the long-standing challenges in healthcare informatics has been the ability to deal with the sheer variety and volume of disparate healthcare data and the increasing need to derive veracity and value out of it. Demystifying Big Data and Machine Learning for Healthcare investigates how healthcare organizations can leverage this tapestry of big data to discover new business value, use cases, and knowledge as well as how big data can be woven into pre-existing business intelligence and analytics efforts. This book focuses on teaching you how to: Develop skills needed to identify and demolish big-data myths Become an expert in separating hype from reality Understand the V's that matter in healthcare and why Harmonize the 4 C's across little and big data Choose data fi delity over data quality Learn how to apply the NRF Framework Master applied machine learning for healthcare Conduct a guided tour of learning algorithms Recognize and be prepared for the future of artificial intelligence in healthcare via best practices, feedback loops, and contextually intelligent agents (CIAs) The variety of data in healthcare spans multiple business workflows, formats (structured, un-, and semi-structured), integration at point of care/need, and integration with existing knowledge. In order to deal with these realities, the authors propose new approaches to creating a knowledge-driven learning organization-based on new and existing strategies, methods and technologies. This book will address the long-standing challenges in healthcare informatics and provide pragmatic recommendations on how to deal with them.

Dieser Band enthalt die Beitrage einer Ringvorlesung Highlights aus der Informatik an der Universitat Dortmund, in der Wissenschaftler, die durch ihre Forschung und didaktischen Fahigkeiten ausgewiesen sind, Glanzlichter aus der neueren Informatikforschung aufbereiteten und sie so Studenten und interessierten Laien zuganglich gemacht haben. Dabei wird das ganze Spektrum von tiefliegenden theoretischen Ergebnissen uber anwendungsorientierte Entwicklungen bis zur uberraschenden Losung altbekannter kombinatorischer Probleme behandelt. Die Autoren zeigen kenntnisreich und bisweilen humorvoll, wie aufregend aktuelle Forschung sein kann "

This book helps and promotes the use of machine learning tools and techniques in econometrics and explains how machine learning can enhance and expand the econometrics toolbox in theory and in practice. Throughout the volume, the authors raise and answer six questions: 1) What are the similarities between existing econometric and machine learning techniques? 2) To what extent can machine learning techniques assist econometric investigation? Specifically, how robust or stable is the prediction from machine learning algorithms given the ever-changing nature of human behavior? 3) Can machine learning techniques assist in testing statistical hypotheses and identifying causal relationships in 'big data? 4) How can existing econometric techniques be extended by incorporating machine learning concepts? 5) How can new econometric tools and approaches be elaborated on based on machine learning techniques? 6) Is it possible to develop machine learning techniques further and make them even more readily applicable in econometrics? As the data structures in economic and financial data become more complex and models become more sophisticated, the book takes a multidisciplinary approach in developing both disciplines of machine learning and econometrics in conjunction, rather than in isolation. This volume is a must-read for scholars, researchers, students, policy-makers, and practitioners, who are using econometrics in theory or in practice.

This book is a guide to productionizing AI solutions using best-of-breed cloud services with workarounds to lower costs. Supplemented with step-by-step instructions covering data import through wrangling to partitioning and modeling through to inference and deployment, and augmented with plenty of Python code samples, the book has been written to accelerate the process of moving from script or notebook to app. From an initial look at the context and ecosystem of AI solutions today, the book drills down from high-level business needs into best practices, working with stakeholders, and agile team collaboration. From there you'll explore data pipeline orchestration, machine and deep learning, including working with and finding shortcuts using artificial neural networks such as AutoML and AutoAI. You'll also learn about the increasing use of NoLo UIs through AI application development, industry case studies, and finally a practical guide to deploying containerized AI solutions. The book is intended for those whose role demands overcoming budgetary barriers or constraints in accessing cloud credits to undertake the often difficult process of developing and deploying an AI solution. What You Will Learn Develop and deliver production-grade AI in one month Deploy AI solutions at a low cost Work around Big Tech dominance and develop MVPs on the cheap Create demo-ready solutions without overly complex python scripts/notebooks Who this book is for: Data scientists and AI consultants with programming skills in Python and driven to succeed in AI.