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001 978-3-642-05177-7
003 DE-He213
005 20170628035137.0
007 cr nn 008mamaa
008 100301s2010 gw | s |||| 0|eng d
020 _a9783642051777
_9978-3-642-05177-7
024 7 _a10.1007/978-3-642-05177-7
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aKoronacki, Jacek.
_eeditor.
245 1 0 _aAdvances in Machine Learning I
_h[electronic resource] :
_bDedicated to the Memory of Professor Ryszard S.Michalski /
_cedited by Jacek Koronacki, Zbigniew W. Raś, Sławomir T. Wierzchoń, Janusz Kacprzyk.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2010.
300 _aXIX, 521 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-949X ;
_v262
505 0 _aIntroductory Chapters -- Ryszard S. Michalski: The Vision and Evolution of Machine Learning -- The AQ Methods for Concept Drift -- Machine Learning Algorithms Inspired by the Work of Ryszard Spencer Michalski -- Inductive Learning: A Combinatorial Optimization Approach -- General Issues -- From Active to Proactive Learning Methods -- Explicit Feature Construction and Manipulation for Covering Rule Learning Algorithms -- Transfer Learning via Advice Taking -- Classification and Beyond -- Determining the Best Classification Algorithm with Recourse to Sampling and Metalearning -- Transductive Learning for Spatial Data Classification -- Beyond Sequential Covering – Boosted Decision Rules -- An Analysis of Relevance Vector Machine Regression -- Cascade Classifiers for Hierarchical Decision Systems -- Creating Rule Ensembles from Automatically-Evolved Rule Induction Algorithms -- Structured Hidden Markov Model versus String Kernel Machines for Symbolic Sequence Classification -- Soft Computing -- Partition Measures for Data Mining -- An Analysis of the FURIA Algorithm for Fuzzy Rule Induction -- Increasing Incompleteness of Data Sets—A Strategy for Inducing Better Rule Sets -- Knowledge Discovery Using Rough Set Theory -- Machine Learning Techniques for Prostate Ultrasound Image Diagnosis -- Segmentation of Breast Cancer Fine Needle Biopsy Cytological Images Using Fuzzy Clustering -- Machine Learning for Robotics -- Automatic Selection of Object Recognition Methods Using Reinforcement Learning -- Comparison of Machine Learning for Autonomous Robot Discovery -- Multistrategy Learning for Robot Behaviours -- Neural Networks and Other Nature Inspired Approaches -- Quo Vadis? Reliable and Practical Rule Extraction from Neural Networks -- Learning and Evolution of Autonomous Adaptive Agents -- Learning and Unlearning in Hopfield-Like Neural Network Performing Boolean Factor Analysis.
520 _aThis is the first 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.
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aArtificial Intelligence (incl. Robotics).
700 1 _aRaś, Zbigniew W.
_eeditor.
700 1 _aWierzchoń, Sławomir T.
_eeditor.
700 1 _aKacprzyk, Janusz.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642051760
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v262
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-05177-7
912 _aZDB-2-ENG
999 _c23193
_d23193