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

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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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.

---

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.

Content:
Front Matter....Pages -
Front Matter....Pages 1-1
Knowledge-Oriented and Distributed Unsupervised Learning for Concept Elicitation....Pages 3-21
Toward Interactive Computations: A Rough-Granular Approach....Pages 23-42
Data Privacy: From Technology to Economics....Pages 43-74
Adapting to Human Gamers Using Coevolution....Pages 75-100
Wisdom of Crowds in the Prisoner’s Dilemma Context....Pages 101-118
Front Matter....Pages 119-119
Towards Multistrategic Statistical Relational Learning....Pages 121-142
About Knowledge and Inference in Logical and Relational Learning....Pages 143-153
Two Examples of Computational Creativity: ILP Multiple Predicate Synthesis and the ‘Assets’ in Theorem Proving....Pages 155-173
Logical Aspects of the Measures of Interestingness of Association Rules....Pages 175-203
Front Matter....Pages 205-205
Clustering the Web 2.0....Pages 207-223
Induction in Multi-Label Text Classification Domains....Pages 225-244
Cluster-Lift Method for Mapping Research Activities over a Concept Tree....Pages 245-257
On Concise Representations of Frequent Patterns Admitting Negation....Pages 259-289
Front Matter....Pages 291-291
A System to Detect Inconsistencies between a Domain Expert’s Different Perspectives on (Classification) Tasks....Pages 293-314
The Dynamics of Multiagent Q-Learning in Commodity Market Resource Allocation....Pages 315-349
Simple Algorithms for Frequent Item Set Mining....Pages 351-369
Monte Carlo Feature Selection and Interdependency Discovery in Supervised Classification....Pages 371-385
Machine Learning Methods in Automatic Image Annotation....Pages 387-411
Front Matter....Pages 413-413
Integrative Probabilistic Evolving Spiking Neural Networks Utilising Quantum Inspired Evolutionary Algorithm: A Computational Framework....Pages 415-425
Machine Learning in Vector Models of Neural Networks....Pages 427-443
Front Matter....Pages 413-413
Nature Inspired Multi-Swarm Heuristics for Multi-Knowledge Extraction....Pages 445-466
Discovering Data Structures Using Meta-learning, Visualization and Constructive Neural Networks....Pages 467-484
Neural Network and Artificial Immune Systems for Malware and Network Intrusion Detection....Pages 485-513
Immunocomputing for Speaker Recognition....Pages 515-529
Back Matter....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.

Content:
Front Matter....Pages -
Front Matter....Pages 1-1
Knowledge-Oriented and Distributed Unsupervised Learning for Concept Elicitation....Pages 3-21
Toward Interactive Computations: A Rough-Granular Approach....Pages 23-42
Data Privacy: From Technology to Economics....Pages 43-74
Adapting to Human Gamers Using Coevolution....Pages 75-100
Wisdom of Crowds in the Prisoner’s Dilemma Context....Pages 101-118
Front Matter....Pages 119-119
Towards Multistrategic Statistical Relational Learning....Pages 121-142
About Knowledge and Inference in Logical and Relational Learning....Pages 143-153
Two Examples of Computational Creativity: ILP Multiple Predicate Synthesis and the ‘Assets’ in Theorem Proving....Pages 155-173
Logical Aspects of the Measures of Interestingness of Association Rules....Pages 175-203
Front Matter....Pages 205-205
Clustering the Web 2.0....Pages 207-223
Induction in Multi-Label Text Classification Domains....Pages 225-244
Cluster-Lift Method for Mapping Research Activities over a Concept Tree....Pages 245-257
On Concise Representations of Frequent Patterns Admitting Negation....Pages 259-289
Front Matter....Pages 291-291
A System to Detect Inconsistencies between a Domain Expert’s Different Perspectives on (Classification) Tasks....Pages 293-314
The Dynamics of Multiagent Q-Learning in Commodity Market Resource Allocation....Pages 315-349
Simple Algorithms for Frequent Item Set Mining....Pages 351-369
Monte Carlo Feature Selection and Interdependency Discovery in Supervised Classification....Pages 371-385
Machine Learning Methods in Automatic Image Annotation....Pages 387-411
Front Matter....Pages 413-413
Integrative Probabilistic Evolving Spiking Neural Networks Utilising Quantum Inspired Evolutionary Algorithm: A Computational Framework....Pages 415-425
Machine Learning in Vector Models of Neural Networks....Pages 427-443
Front Matter....Pages 413-413
Nature Inspired Multi-Swarm Heuristics for Multi-Knowledge Extraction....Pages 445-466
Discovering Data Structures Using Meta-learning, Visualization and Constructive Neural Networks....Pages 467-484
Neural Network and Artificial Immune Systems for Malware and Network Intrusion Detection....Pages 485-513
Immunocomputing for Speaker Recognition....Pages 515-529
Back Matter....Pages -
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