Machine Learning: ECML'97


Book Description

This book constitutes the refereed proceedings of the Ninth European Conference on Machine Learning, ECML-97, held in Prague, Czech Republic, in April 1997. This volume presents 26 revised full papers selected from a total of 73 submissions. Also included are an abstract and two papers corresponding to the invited talks as well as descriptions from four satellite workshops. The volume covers the whole spectrum of current machine learning issues.




Machine Learning: ECML 2004


Book Description

This book constitutes the refereed proceedings of the 15th European Conference on Machine Learning, ECML 2004, held in Pisa, Italy, in September 2004, jointly with PKDD 2004. The 45 revised full papers and 6 revised short papers presented together with abstracts of 5 invited talks were carefully reviewed and selected from 280 papers submitted to ECML and 107 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.







Machine Intelligence


Book Description

This book brings together the contributions of leading researchers inthe field of machine intelligence, covering areas such as fuzzy logic, neural networks, evolutionary computation and hybrid systems.There is wide coverage of the subject from simple tools, throughindustrial applications, to applications in high-level intelligentsystems which are biologically motivated, such as humanoid robots (andselected parts of these systems, like the visual cortex). Readers willgain a comprehensive overview of the issues in machine intelligence, afield which promises to play a very important role in the informationsociety of the future




Machine Learning: ECML 2000


Book Description

The biennial European Conference on Machine Learning (ECML) series is intended to provide an international forum for the discussion of the latest high quality research results in machine learning and is the major European scienti?c event in the ?eld. The eleventh conference (ECML 2000) held in Barcelona, Catalonia, Spain from May 31 to June 2, 2000, has continued this tradition by attracting high quality papers from around the world. Scientists from 21 countries submitted 100 papers to ECML 2000, from which 20 were selected for long oral presentations and 23 for short oral presentations. This selection was based on the recommendations of at least two reviewers for each submitted paper. It is worth noticing that the number of papers reporting applications of machine learning has increased in comparison to past ECML conferences. We believe this fact shows the growing maturity of the ?eld. This volume contains the 43 accepted papers as well as the invited talks by Katharina Morik from the University of Dortmund and Pedro Domingos from the University of Washington at Seattle. In addition, three workshops were jointly organized by ECML 2000 and the European Network of Excellence - net: “Dealing with Structured Data in Machine Learning and Statistics W- stites”, “Machine Learning in the New Information Age” , and “Meta-Learning: Building Automatic Advice Strategies for Model Selection and Method Com- nation”.




An Introduction to Machine Learning


Book Description

This textbook offers a comprehensive introduction to Machine Learning techniques and algorithms. This Third Edition covers newer approaches that have become highly topical, including deep learning, and auto-encoding, introductory information about temporal learning and hidden Markov models, and a much more detailed treatment of reinforcement learning. The book is written in an easy-to-understand manner with many examples and pictures, and with a lot of practical advice and discussions of simple applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, rule-induction programs, artificial neural networks, support vector machines, boosting algorithms, unsupervised learning (including Kohonen networks and auto-encoding), deep learning, reinforcement learning, temporal learning (including long short-term memory), hidden Markov models, and the genetic algorithm. Special attention is devoted to performance evaluation, statistical assessment, and to many practical issues ranging from feature selection and feature construction to bias, context, multi-label domains, and the problem of imbalanced classes.




Machine Learning and Its Applications


Book Description

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.




Machine Learning and Data Mining in Pattern Recognition


Book Description

TheInternationalConferenceonMachineLearningandDataMining(MLDM)is the third meeting in a series of biennial events, which started in 1999, organized by the Institute of Computer Vision and Applied Computer Sciences (IBaI) in Leipzig. MLDM began as a workshop and is now a conference, and has brought the topic of machine learning and data mining to the attention of the research community. Seventy-?ve papers were submitted to the conference this year. The program committeeworkedhardtoselectthemostprogressiveresearchinafairandc- petent review process which led to the acceptance of 33 papers for presentation at the conference. The 33 papers in these proceedings cover a wide variety of topics related to machine learning and data mining. The two invited talks deal with learning in case-based reasoning and with mining for structural data. The contributed papers can be grouped into nine areas: support vector machines; pattern dis- very; decision trees; clustering; classi?cation and retrieval; case-based reasoning; Bayesian models and methods; association rules; and applications. We would like to express our appreciation to the reviewers for their precise andhighlyprofessionalwork.WearegratefultotheGermanScienceFoundation for its support of the Eastern European researchers. We appreciate the help and understanding of the editorial sta? at Springer Verlag, and in particular Alfred Hofmann,whosupportedthepublicationoftheseproceedingsintheLNAIseries. Last, but not least, we wish to thank all the speakers and participants who contributed to the success of the conference.




Foundations of Intelligent Systems


Book Description

This book constitutes the refereed proceedings of the 10th International Symposium on Methodologies for Intelligent Systems, ISMIS'97, held in Charlotte, NC, USA, in October 1997. The 57 revised full papers were selected from a total of 117 submissions. Also included are four invited papers. Among the topics covered are intelligent information systems, approximate reasoning, evolutionary computation, knowledge representation and integration, learning and knowledge discovery, AI-Logics, discovery systems, data mining, query processing, etc.




Algorithms for Reinforcement Learning


Book Description

Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations. Table of Contents: Markov Decision Processes / Value Prediction Problems / Control / For Further Exploration