## Advances in Machine Learning I: Dedicated to the Memory of Professor Ryszard S. MichalskiJacek Koronacki, Zbigniew W Ras, Slawomir T. Wierzchon Professor Richard S. Michalski passed away on September 20, 2007. Once we learned about his untimely death we immediately realized that we would no longer have with us a truly exceptional scholar and researcher who for several decades had been inf- encing the work of numerous scientists all over the world - not only in his area of expertise, notably machine learning, but also in the broadly understood areas of data analysis, data mining, knowledge discovery and many others. In fact, his influence was even much broader due to his creative vision, integrity, scientific excellence and exceptionally wide intellectual horizons which extended to history, political science and arts. Professor Michalski’s death was a particularly deep loss to the whole Polish sci- tific community and the Polish Academy of Sciences in particular. After graduation, he began his research career at the Institute of Automatic Control, Polish Academy of Science in Warsaw. In 1970 he left his native country and hold various prestigious positions at top US universities. His research gained impetus and he soon established himself as a world authority in his areas of interest – notably, he was widely cons- ered a father of machine learning. |

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### Contents

The Vision and Evolution of Machine Learning | 3 |

The AQ Methods for Concept Drift | 23 |

Machine Learning Algorithms Inspired by the Work of Ryszard Spencer Michalski | 49 |

A Combinatorial Optimization Approach | 75 |

General Issues | 94 |

From Active to Proactive Learning Methods | 97 |

Explicit Feature Construction and Manipulation for Covering Rule Learning Algorithms | 121 |

Transfer Learning via Advice Taking | 147 |

Partition Measures for Data Mining | 299 |

An Analysis of the FURIA Algorithm for Fuzzy Rule Induction | 320 |

Increasing Incompleteness of Data Sets A Strategy for Inducing Better Rule Sets | 345 |

Knowledge Discovery Using Rough Set Theory | 367 |

Image Diagnosis | 384 |

Segmentation of Breast Cancer Fine Needle Biopsy Cytological Images Using Fuzzy Clustering | 405 |

Machine Learning for Robotics | 418 |

Automatic Selection of Object Recognition Methods Using Reinforcement Learning | 421 |

Classification and Beyond | 171 |

Determining the Best Classification Algorithm with Recourse to Sampling and Metalearning | 172 |

Transductive Learning for Spatial Data Classification | 189 |

Beyond Sequential Covering Boosted Decision Rules | 209 |

An Analysis of Relevance Vector Machine Regression | 226 |

Cascade Classifiers for Hierarchical Decision Systems | 247 |

Creating Rule Ensembles from AutomaticallyEvolved Rule Induction Algorithms | 257 |

Structured Hidden Markov Model versus String Kernel Machines for Symbolic Sequence Classification | 275 |

Soft Computing | 296 |

Comparison of Machine Learning for Autonomous Robot Discovery | 440 |

Multistrategy Learning for Robot Behaviours | 457 |

Neural Networks and Other Nature Inspired Approaches | 477 |

Quo Vadis? Reliable and Practical Rule Extraction from Neural Networks | 479 |

Learning and Evolution of Autonomous Adaptive Agents | 491 |

Learning and Unlearning in HopﬁeldLike Neural Network Performing Boolean Factor Analysis | 501 |

Author Index | 519 |

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### Common terms and phrases

accuracy analysis application approach classiﬁcation classiﬁcation models classifier CLIP4 clustering Computer concept drift construction covering data mining data set database decision rules decision trees deﬁned deﬁnition described domain ensemble equation error evaluation evolutionary evolutionary algorithm factors ﬁnd ﬁrst FURIA fuzzy genetic algorithm Heidelberg IEEE International iteration knowledge label learner learning algorithms LNCS LNAI Lyapunov function Machine Learning matrix measure Michalski missing attribute values negative examples neural networks neurons objects optimal oracle pair parameters partition performance positive examples prediction problem programming regression reinforcement learning relation relevant robot Rough Set Theory rule extraction rule induction rule induction algorithms rule learning rule sets S-HMM sampling Section segmentation selection sequences set covering problem signiﬁcant signiﬁcantly solution space spatial speciﬁc Springer step subset Table target task techniques tion training examples training set transductive true false variables vector weighted