000 04036nam a22004935i 4500
001 978-3-540-71984-7
003 DE-He213
005 20161121231203.0
007 cr nn 008mamaa
008 100301s2007 gw | s |||| 0|eng d
020 _a9783540719847
_9978-3-540-71984-7
024 7 _a10.1007/978-3-540-71984-7
_2doi
050 4 _aTA329-348
050 4 _aTA640-643
072 7 _aTBJ
_2bicssc
072 7 _aMAT003000
_2bisacsh
082 0 4 _a519
_223
245 1 0 _aChallenges for Computational Intelligence
_h[electronic resource] /
_cedited by Włodzisław Duch, Jacek Mańdziuk.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2007.
300 _aXII, 487 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 ;
_v63
505 0 _aWhat Is Computational Intelligence and Where Is It Going? -- New Millennium AI and the Convergence of History -- The Challenges of Building Computational Cognitive Architectures -- Programming a Parallel Computer: The Ersatz Brain Project -- The Human Brain as a Hierarchical Intelligent Control System -- Artificial Brain and OfficeMate TR based on Brain Information Processing Mechanism -- Natural Intelligence and Artificial Intelligence: Bridging the Gap between Neurons and Neuro-Imaging to Understand Intelligent Behaviour -- Computational Scene Analysis -- Brain-, Gene-, and Quantum Inspired Computational Intelligence: Challenges and Opportunities -- The Science of Pattern Recognition. Achievements and Perspectives -- Towards Comprehensive Foundations of Computational Intelligence -- Knowledge-Based Clustering in Computational Intelligence -- Generalization in Learning from Examples -- A Trend on Regularization and Model Selection in Statistical Learning: A Bayesian Ying Yang Learning Perspective -- Computational Intelligence in Mind Games -- Computer Go: A Grand Challenge to AI -- Noisy Chaotic Neural Networks for Combinatorial Optimization.
520 _aIn the year 1900 at the International Congress of Mathematicians in Paris David Hilbert delivered what is now considered the most important talk ever given in the history of mathematics, proposing 23 major problems worth working at in the future. One hundred years later the impact of this talk is still strong: some problems have been solved, new problems have been added, but the direction once set -- identify the most important problems and focus on them -- is still actual. Computational Intelligence (CI) is used as a name to cover many existing branches of science, with artificial neural networks, fuzzy systems and evolutionary computation forming its core. In recent years CI has been extended by adding many other subdisciplines and it became quite obvious that this new field also requires a series of challenging problems that will give it a sense of direction. Without setting up clear goals and yardsticks to measure progress on the way many research efforts are wasted. The book written by top experts in CI provides such clear directions and the much-needed focus on the most important and challenging research issues, showing a roadmap how to achieve ambitious goals.
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aApplied mathematics.
650 0 _aEngineering mathematics.
650 1 4 _aEngineering.
650 2 4 _aAppl.Mathematics/Computational Methods of Engineering.
650 2 4 _aArtificial Intelligence (incl. Robotics).
700 1 _aDuch, Włodzisław.
_eeditor.
700 1 _aMańdziuk, Jacek.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783540719830
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v63
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-540-71984-7
912 _aZDB-2-ENG
950 _aEngineering (Springer-11647)
999 _c509835
_d509835