Advances in Machine Learning II: Dedicated to the Memory of Professor Ryszard S. Michalski

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Jacek Koronacki, Zbigniew W. Ras, Slawomir T. Wierzchon
Springer Science & Business Media, Dec 24, 2009 - Computers - 529 pages
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This is the second volume of a large two-volume editorial project we wish to dedicate to the memory of the late Professor Ryszard S. Michalski who passed away in 2007. He was one of the fathers of machine learning, an exciting and relevant, both from the practical and theoretical points of view, area in modern computer science and information technology. His research career started in the mid-1960s in Poland, in the Institute of Automation, Polish Academy of Sciences in Warsaw, Poland. He left for the USA in 1970, and since then had worked there at various universities, notably, at the University of Illinois at Urbana – Champaign and finally, until his untimely death, at George Mason University. We, the editors, had been lucky to be able to meet and collaborate with Ryszard for years, indeed some of us knew him when he was still in Poland. After he started working in the USA, he was a frequent visitor to Poland, taking part at many conferences until his death. We had also witnessed with a great personal pleasure honors and awards he had received over the years, notably when some years ago he was elected Foreign Member of the Polish Academy of Sciences among some top scientists and scholars from all over the world, including Nobel prize winners.

Professor Michalski’s research results influenced very strongly the development of machine learning, data mining, and related areas. Also, he inspired many established and younger scholars and scientists all over the world.

We feel very happy that so many top scientists from all over the world agreed to pay the last tribute to Professor Michalski by writing papers in their areas of research. These papers will constitute the most appropriate tribute to Professor Michalski, a devoted scholar and researcher. Moreover, we believe that they will inspire many newcomers and younger researchers in the area of broadly perceived machine learning, data analysis and data mining.

The papers included in the two volumes, Machine Learning I and Machine Learning II, cover diverse topics, and various aspects of the fields involved. For convenience of the potential readers, we will now briefly summarize the contents of the particular chapters.


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KnowledgeOriented and Distributed Unsupervised Learning for Concept Elicitation
A RoughGranular Approach
From Technology to Economics
Adapting to Human Gamers Using Coevolution
Wisdom of Crowds in the Prisoners Dilemma Context
Logical and Relational Learning and Beyond
Towards Multistrategic Statistical Relational Learning
About Knowledge and Inference in Logical and Relational Learning
Classification and Beyond
A System to Detect Inconsistencies between a Domain Experts Different Perspectives on Classification Tasks
The Dynamics of Multiagent QLearning in Commodity Market Resource Allocation
Simple Algorithms for Frequent Item Set Mining
Monte Carlo Feature Selection and Interdependency Discovery in Supervised Classification
Machine Learning Methods in Automatic Image Annotation
Neural Networks and Other Nature Inspired Approaches
A Computational Framework

ILP Multiple Predicate Synthesis and the Assets in Theorem Proving
Logical Aspects of the Measures of Interestingness of Association Rules
Text and Web Mining
Clustering the Web 20
Induction in MultiLabel Text Classification Domains
ClusterLift Method for Mapping Research Activities over a Concept Tree
On Concise Representations of Frequent Patterns Admitting Negation
Machine Learning in Vector Models of Neural Networks
Nature Inspired MultiSwarm Heuristics for MultiKnowledge Extraction
Discovering Data Structures Using Metalearning Visualization and Constructive Neural Networks
Neural Network and Artificial Immune Systems for Malware and Network Intrusion Detection
Immunocomputing for Speaker Recognition
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